Ido Dagan


2021

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CDˆ2CR: Co-reference resolution across documents and domains
James Ravenscroft | Amanda Clare | Arie Cattan | Ido Dagan | Maria Liakata
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models for this task assume that all documents are of the same type (e.g. news articles) or fall under the same theme. However, it is also desirable to perform CDCR across different domains (type or theme). A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them. Identifying the same entities and corresponding concepts in both scientific articles and news can help scientists understand how their work is represented in mainstream media. We propose a new task and English language dataset for cross-document cross-domain co-reference resolution (CDˆ2CR). The task aims to identify links between entities across heterogeneous document types. We show that in this cross-domain, cross-document setting, existing CDCR models do not perform well and we provide a baseline model that outperforms current state-of-the-art CDCR models on CDˆ2CR. Our data set, annotation tool and guidelines as well as our model for cross-document cross-domain co-reference are all supplied as open access open source resources.

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Teach the Rules, Provide the Facts: Targeted Relational-knowledge Enhancement for Textual Inference
Ohad Rozen | Shmuel Amar | Vered Shwartz | Ido Dagan
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

We present InferBert, a method to enhance transformer-based inference models with relevant relational knowledge. Our approach facilitates learning generic inference patterns requiring relational knowledge (e.g. inferences related to hypernymy) during training, while injecting on-demand the relevant relational facts (e.g. pangolin is an animal) at test time. We apply InferBERT to the NLI task over a diverse set of inference types (hypernymy, location, color, and country of origin), for which we collected challenge datasets. In this setting, InferBert succeeds to learn general inference patterns, from a relatively small number of training instances, while not hurting performance on the original NLI data and substantially outperforming prior knowledge enhancement models on the challenge data. It further applies its inferences successfully at test time to previously unobserved entities. InferBert is computationally more efficient than most prior methods, in terms of number of parameters, memory consumption and training time.

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Realistic Evaluation Principles for Cross-document Coreference Resolution
Arie Cattan | Alon Eirew | Gabriel Stanovsky | Mandar Joshi | Ido Dagan
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

We point out that common evaluation practices for cross-document coreference resolution have been unrealistically permissive in their assumed settings, yielding inflated results. We propose addressing this issue via two evaluation methodology principles. First, as in other tasks, models should be evaluated on predicted mentions rather than on gold mentions. Doing this raises a subtle issue regarding singleton coreference clusters, which we address by decoupling the evaluation of mention detection from that of coreference linking. Second, we argue that models should not exploit the synthetic topic structure of the standard ECB+ dataset, forcing models to confront the lexical ambiguity challenge, as intended by the dataset creators. We demonstrate empirically the drastic impact of our more realistic evaluation principles on a competitive model, yielding a score which is 33 F1 lower compared to evaluating by prior lenient practices.

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Denoising Word Embeddings by Averaging in a Shared Space
Avi Caciularu | Ido Dagan | Jacob Goldberger
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

We introduce a new approach for smoothing and improving the quality of word embeddings. We consider a method of fusing word embeddings that were trained on the same corpus but with different initializations. We project all the models to a shared vector space using an efficient implementation of the Generalized Procrustes Analysis (GPA) procedure, previously used in multilingual word translation. Our word representation demonstrates consistent improvements over the raw models as well as their simplistic average, on a range of tasks. As the new representations are more stable and reliable, there is a noticeable improvement in rare word evaluations.

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Extending Multi-Document Summarization Evaluation to the Interactive Setting
Ori Shapira | Ramakanth Pasunuru | Hadar Ronen | Mohit Bansal | Yael Amsterdamer | Ido Dagan
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results. Different ideas for interactive summarization have been proposed in previous work but these solutions are highly divergent and incomparable. In this paper, we develop an end-to-end evaluation framework for interactive summarization, focusing on expansion-based interaction, which considers the accumulating information along a user session. Our framework includes a procedure of collecting real user sessions, as well as evaluation measures relying on summarization standards, but adapted to reflect interaction. All of our solutions and resources are available publicly as a benchmark, allowing comparison of future developments in interactive summarization, and spurring progress in its methodological evaluation. We demonstrate the use of our framework by evaluating and comparing baseline implementations that we developed for this purpose, which will serve as part of our benchmark. Our extensive experimentation and analysis motivate the proposed evaluation framework design and support its viability.

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WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia
Alon Eirew | Arie Cattan | Ido Dagan
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing. However, existing corpora for this task are scarce and relatively small, while annotating only modest-size clusters of documents belonging to the same topic. To complement these resources and enhance future research, we present Wikipedia Event Coreference (WEC), an efficient methodology for gathering a large-scale dataset for cross-document event coreference from Wikipedia, where coreference links are not restricted within predefined topics. We apply this methodology to the English Wikipedia and extract our large-scale WEC-Eng dataset. Notably, our dataset creation method is generic and can be applied with relatively little effort to other Wikipedia languages. To set baseline results, we develop an algorithm that adapts components of state-of-the-art models for within-document coreference resolution to the cross-document setting. Our model is suitably efficient and outperforms previously published state-of-the-art results for the task.

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Cross-document Coreference Resolution over Predicted Mentions
Arie Cattan | Alon Eirew | Gabriel Stanovsky | Mandar Joshi | Ido Dagan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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QANom: Question-Answer driven SRL for Nominalizations
Ayal Klein | Jonathan Mamou | Valentina Pyatkin | Daniela Stepanov | Hangfeng He | Dan Roth | Luke Zettlemoyer | Ido Dagan
Proceedings of the 28th International Conference on Computational Linguistics

We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom. This scheme extends the QA-SRL formalism (He et al., 2015), modeling the relations between nominalizations and their arguments via natural language question-answer pairs. We construct the first QANom dataset using controlled crowdsourcing, analyze its quality and compare it to expertly annotated nominal-SRL annotations, as well as to other QA-driven annotations. In addition, we train a baseline QANom parser for identifying nominalizations and labeling their arguments with question-answer pairs. Finally, we demonstrate the extrinsic utility of our annotations for downstream tasks using both indirect supervision and zero-shot settings.

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Paraphrasing vs Coreferring: Two Sides of the Same Coin
Yehudit Meged | Avi Caciularu | Vered Shwartz | Ido Dagan
Findings of the Association for Computational Linguistics: EMNLP 2020

We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution. First, we used annotations from an event coreference dataset as distant supervision to re-score heuristically-extracted predicate paraphrases. The new scoring gained more than 18 points in average precision upon their ranking by the original scoring method. Then, we used the same re-ranking features as additional inputs to a state-of-the-art event coreference resolution model, which yielded modest but consistent improvements to the model’s performance. The results suggest a promising direction to leverage data and models for each of the tasks to the benefit of the other.

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Controlled Crowdsourcing for High-Quality QA-SRL Annotation
Paul Roit | Ayal Klein | Daniela Stepanov | Jonathan Mamou | Julian Michael | Gabriel Stanovsky | Luke Zettlemoyer | Ido Dagan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen. Recently, a large-scale crowdsourced QA-SRL corpus and a trained parser were released. Trying to replicate the QA-SRL annotation for new texts, we found that the resulting annotations were lacking in quality, particularly in coverage, making them insufficient for further research and evaluation. In this paper, we present an improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase. Applying this protocol to QA-SRL yielded high-quality annotation with drastically higher coverage, producing a new gold evaluation dataset. We believe that our annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations.

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Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP
Oren Sar Shalom | Alexander Panchenko | Cicero dos Santos | Varvara Logacheva | Alessandro Moschitti | Ido Dagan
Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP

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QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
Valentina Pyatkin | Ayal Klein | Reut Tsarfaty | Ido Dagan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding. However, annotating discourse relations typically requires expert annotators. Recently, different semantic aspects of a sentence have been represented and crowd-sourced via question-and-answer (QA) pairs. This paper proposes a novel representation of discourse relations as QA pairs, which in turn allows us to crowd-source wide-coverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers. Based on our proposed representation, we collect a novel and wide-coverage QADiscourse dataset, and present baseline algorithms for predicting QADiscourse relations.

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Within-Between Lexical Relation Classification
Oren Barkan | Avi Caciularu | Ido Dagan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose the novel Within-Between Relation model for recognizing lexical-semantic relations between words. Our model integrates relational and distributional signals, forming an effective sub-space representation for each relation. We show that the proposed model is competitive and outperforms other baselines, across various benchmarks.

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CoRefi: A Crowd Sourcing Suite for Coreference Annotation
Ari Bornstein | Arie Cattan | Ido Dagan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Coreference annotation is an important, yet expensive and time consuming, task, which often involved expert annotators trained on complex decision guidelines. To enable cheaper and more efficient annotation, we present CoRefi, a web-based coreference annotation suite, oriented for crowdsourcing. Beyond the core coreference annotation tool, CoRefi provides guided onboarding for the task as well as a novel algorithm for a reviewing phase. CoRefi is open source and directly embeds into any website, including popular crowdsourcing platforms. CoRefi Demo: aka.ms/corefi Video Tour: aka.ms/corefivideo Github Repo: https://github.com/aribornstein/corefi

2019

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Better Rewards Yield Better Summaries: Learning to Summarise Without References
Florian Böhm | Yang Gao | Christian M. Meyer | Ori Shapira | Ido Dagan | Iryna Gurevych
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Reinforcement Learning (RL)based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during training. However, summaries with high ROUGE scores often receive low human judgement. To find a better reward function that can guide RL to generate human-appealing summaries, we learn a reward function from human ratings on 2,500 summaries. Our reward function only takes the document and system summary as input. Hence, once trained, it can be used to train RL based summarisation systems without using any reference summaries. We show that our learned rewards have significantly higher correlation with human ratings than previous approaches. Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summaries with higher human ratings. The learned reward function and our source code are available at https://github.com/yg211/summary-reward-no-reference.

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ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System
Oren Pereg | Daniel Korat | Moshe Wasserblat | Jonathan Mamou | Ido Dagan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We present ABSApp, a portable system for weakly-supervised aspect-based sentiment ex- traction. The system is interpretable and user friendly and does not require labeled training data, hence can be rapidly and cost-effectively used across different domains in applied setups. The system flow includes three stages: First, it generates domain-specific aspect and opinion lexicons based on an unlabeled dataset; second, it enables the user to view and edit those lexicons (weak supervision); and finally, it enables the user to select an unlabeled target dataset from the same domain, classify it, and generate an aspect-based sentiment report. ABSApp has been successfully used in a number of real-life use cases, among them movie review analysis and convention impact analysis.

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Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Ori Shapira | David Gabay | Yang Gao | Hadar Ronen | Ramakanth Pasunuru | Mohit Bansal | Yael Amsterdamer | Ido Dagan
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Conducting a manual evaluation is considered an essential part of summary evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively compares system summaries to references, has been perceived as very reliable, providing objective scores. Yet, due to the high cost of the Pyramid method and the required expertise, researchers resorted to cheaper and less thorough manual evaluation methods, such as Responsiveness and pairwise comparison, attainable via crowdsourcing. We revisit the Pyramid approach, proposing a lightweight sampling-based version that is crowdsourcable. We analyze the performance of our method in comparison to original expert-based Pyramid evaluations, showing higher correlation relative to the common Responsiveness method. We release our crowdsourced Summary-Content-Units, along with all crowdsourcing scripts, for future evaluations.

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Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
Amit Moryossef | Yoav Goldberg | Ido Dagan
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (realization). Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. We propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. For training a plan-to-text generator, we present a method for matching reference texts to their corresponding text plans. For inference time, we describe a method for selecting high-quality text plans for new inputs. We implement and evaluate our approach on the WebNLG benchmark. Our results demonstrate that decoupling text planning from neural realization indeed improves the system’s reliability and adequacy while maintaining fluent output. We observe improvements both in BLEU scores and in manual evaluations. Another benefit of our approach is the ability to output diverse realizations of the same input, paving the way to explicit control over the generated text structure.

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Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets
Ohad Rozen | Vered Shwartz | Roee Aharoni | Ido Dagan
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Phenomenon-specific “adversarial” datasets have been recently designed to perform targeted stress-tests for particular inference types. Recent work (Liu et al., 2019a) proposed that such datasets can be utilized for training NLI and other types of models, often allowing to learn the phenomenon in focus and improve on the challenge dataset, indicating a “blind spot” in the original training data. Yet, although a model can improve in such a training process, it might still be vulnerable to other challenge datasets targeting the same phenomenon but drawn from a different distribution, such as having a different syntactic complexity level. In this work, we extend this method to drive conclusions about a model’s ability to learn and generalize a target phenomenon rather than to “learn” a dataset, by controlling additional aspects in the adversarial datasets. We demonstrate our approach on two inference phenomena – dative alternation and numerical reasoning, elaborating, and in some cases contradicting, the results of Liu et al.. Our methodology enables building better challenge datasets for creating more robust models, and may yield better model understanding and subsequent overarching improvements.

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Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion
Jonathan Mamou | Oren Pereg | Moshe Wasserblat | Ido Dagan
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP

In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set expansion. In addition, we present a novel and unique dataset for intrinsic evaluation of corpus-based term set expansion algorithms. We show that, over this dataset, our algorithm provides up to 5 mean average precision points over the best baseline.

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How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature
Simeng Sun | Ori Shapira | Ido Dagan | Ani Nenkova
Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation

We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths. This is due to a non-linear pattern between ROUGE F1 and summary length. To alleviate the effect of length during evaluation, we have proposed a new method which normalizes the ROUGE F1 scores of a system by that of a random system with same average output length. A pilot human evaluation has shown that humans prefer short summaries in terms of the verbosity of a summary but overall consider longer summaries to be of higher quality. While human evaluations are more expensive in time and resources, it is clear that normalization, such as the one we proposed for automatic evaluation, will make human evaluations more meaningful.

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Revisiting the Binary Linearization Technique for Surface Realization
Yevgeniy Puzikov | Claire Gardent | Ido Dagan | Iryna Gurevych
Proceedings of the 12th International Conference on Natural Language Generation

End-to-end neural approaches have achieved state-of-the-art performance in many natural language processing (NLP) tasks. Yet, they often lack transparency of the underlying decision-making process, hindering error analysis and certain model improvements. In this work, we revisit the binary linearization approach to surface realization, which exhibits more interpretable behavior, but was falling short in terms of prediction accuracy. We show how enriching the training data to better capture word order constraints almost doubles the performance of the system. We further demonstrate that encoding both local and global prediction contexts yields another considerable performance boost. With the proposed modifications, the system which ranked low in the latest shared task on multilingual surface realization now achieves best results in five out of ten languages, while being on par with the state-of-the-art approaches in others.

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Improving Quality and Efficiency in Plan-based Neural Data-to-text Generation
Amit Moryossef | Yoav Goldberg | Ido Dagan
Proceedings of the 12th International Conference on Natural Language Generation

We follow the step-by-step approach to neural data-to-text generation proposed by Moryossef et al (2019), in which the generation process is divided into a text planning stage followed by a plan realization stage. We suggest four extensions to that framework: (1) we introduce a trainable neural planning component that can generate effective plans several orders of magnitude faster than the original planner; (2) we incorporate typing hints that improve the model’s ability to deal with unseen relations and entities; (3) we introduce a verification-by-reranking stage that substantially improves the faithfulness of the resulting texts; (4) we incorporate a simple but effective referring expression generation module. These extensions result in a generation process that is faster, more fluent, and more accurate.

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Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
Tobias Falke | Leonardo F. R. Ribeiro | Prasetya Ajie Utama | Ido Dagan | Iryna Gurevych
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

While recent progress on abstractive summarization has led to remarkably fluent summaries, factual errors in generated summaries still severely limit their use in practice. In this paper, we evaluate summaries produced by state-of-the-art models via crowdsourcing and show that such errors occur frequently, in particular with more abstractive models. We study whether textual entailment predictions can be used to detect such errors and if they can be reduced by reranking alternative predicted summaries. That leads to an interesting downstream application for entailment models. In our experiments, we find that out-of-the-box entailment models trained on NLI datasets do not yet offer the desired performance for the downstream task and we therefore release our annotations as additional test data for future extrinsic evaluations of NLI.

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Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution
Shany Barhom | Vered Shwartz | Alon Eirew | Michael Bugert | Nils Reimers | Ido Dagan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task’s importance, research focus was given mostly to within-document entity coreference, with rather little attention to the other variants. We propose a neural architecture for cross-document coreference resolution. Inspired by Lee et al. (2012), we jointly model entity and event coreference. We represent an event (entity) mention using its lexical span, surrounding context, and relation to entity (event) mentions via predicate-arguments structures. Our model outperforms the previous state-of-the-art event coreference model on ECB+, while providing the first entity coreference results on this corpus. Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model’s success.

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Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition
Vered Shwartz | Ido Dagan
Transactions of the Association for Computational Linguistics, Volume 7

Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a broad range of textual representations for their capacity to address these issues. We found that, as expected, contextualized word representations perform better than static word embeddings, more so on detecting meaning shift than in recovering implicit information, in which their performance is still far from that of humans. Our evaluation suite, consisting of six tasks related to lexical composition effects, can serve future research aiming to improve representations.

2018

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SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings
Jonathan Mamou | Oren Pereg | Moshe Wasserblat | Ido Dagan | Yoav Goldberg | Alon Eirew | Yael Green | Shira Guskin | Peter Izsak | Daniel Korat
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to end workflow for term set expansion. It enables users to easily select a seed set of terms, expand it, view the expanded set, validate it, re-expand the validated set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. SetExpander has been used for solving real-life use cases including integration in an automated recruitment system and an issues and defects resolution system. A video demo of SetExpander is available at https://drive.google.com/open?id=1e545bB87Autsch36DjnJHmq3HWfSd1Rv .

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Supervised Open Information Extraction
Gabriel Stanovsky | Julian Michael | Luke Zettlemoyer | Ido Dagan
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model outperforms the existing state-of-the-art Open IE systems on benchmark datasets.

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Crowdsourcing Question-Answer Meaning Representations
Julian Michael | Gabriel Stanovsky | Luheng He | Ido Dagan | Luke Zettlemoyer
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, and QA-SRL) along with many previously under-resourced ones, including implicit arguments and relations. We also report baseline models for question generation and answering, and summarize a recent approach for using QAMR labels to improve an Open IE system. These results suggest the freely available QAMR data and annotation scheme should support significant future work.

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Automatic Thesaurus Construction for Modern Hebrew
Chaya Liebeskind | Ido Dagan | Jonathan Schler
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations
Vered Shwartz | Ido Dagan
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.

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Zero-Shot Transfer Learning for Event Extraction
Lifu Huang | Heng Ji | Kyunghyun Cho | Ido Dagan | Sebastian Riedel | Clare Voss
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Most previous supervised event extraction methods have relied on features derived from manual annotations, and thus cannot be applied to new event types without extra annotation effort. We take a fresh look at event extraction and model it as a generic grounding problem: mapping each event mention to a specific type in a target event ontology. We design a transferable architecture of structural and compositional neural networks to jointly represent and map event mentions and types into a shared semantic space. Based on this new framework, we can select, for each event mention, the event type which is semantically closest in this space as its type. By leveraging manual annotations available for a small set of existing event types, our framework can be applied to new unseen event types without additional manual annotations. When tested on 23 unseen event types, our zero-shot framework, without manual annotations, achieved performance comparable to a supervised model trained from 3,000 sentences annotated with 500 event mentions.

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Evaluating Multiple System Summary Lengths: A Case Study
Ori Shapira | David Gabay | Hadar Ronen | Judit Bar-Ilan | Yael Amsterdamer | Ani Nenkova | Ido Dagan
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Practical summarization systems are expected to produce summaries of varying lengths, per user needs. While a couple of early summarization benchmarks tested systems across multiple summary lengths, this practice was mostly abandoned due to the assumed cost of producing reference summaries of multiple lengths. In this paper, we raise the research question of whether reference summaries of a single length can be used to reliably evaluate system summaries of multiple lengths. For that, we have analyzed a couple of datasets as a case study, using several variants of the ROUGE metric that are standard in summarization evaluation. Our findings indicate that the evaluation protocol in question is indeed competitive. This result paves the way to practically evaluating varying-length summaries with simple, possibly existing, summarization benchmarks.

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Semantics as a Foreign Language
Gabriel Stanovsky | Ido Dagan
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect. To that end, we first generalize syntactic linearization techniques to account for the richer semantic dependency graph structure. Following, we design a neural sequence-to-sequence framework which can effectively recover our graph linearizations, performing almost on-par with previous SDP state-of-the-art while requiring less parallel training annotations. Beyond SDP, our linearization technique opens the door to integration of graph-based semantic representations as features in neural models for downstream applications.

2017

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A Consolidated Open Knowledge Representation for Multiple Texts
Rachel Wities | Vered Shwartz | Gabriel Stanovsky | Meni Adler | Ori Shapira | Shyam Upadhyay | Dan Roth | Eugenio Martinez Camara | Iryna Gurevych | Ido Dagan
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner. We do so by consolidating OIE extractions using entity and predicate coreference, while modeling information containment between coreferring elements via lexical entailment. We suggest that generating OKR structures can be a useful step in the NLP pipeline, to give semantic applications an easy handle on consolidated information across multiple texts.

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Neural Disambiguation of Causal Lexical Markers Based on Context
Eugenio Martínez-Cámara | Vered Shwartz | Iryna Gurevych | Ido Dagan
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

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Acquiring Predicate Paraphrases from News Tweets
Vered Shwartz | Gabriel Stanovsky | Ido Dagan
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

We present a simple method for ever-growing extraction of predicate paraphrases from news headlines in Twitter. Analysis of the output of ten weeks of collection shows that the accuracy of paraphrases with different support levels is estimated between 60-86%. We also demonstrate that our resource is to a large extent complementary to existing resources, providing many novel paraphrases. Our resource is publicly available, continuously expanding based on daily news.

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Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets
Gabriel Stanovsky | Judith Eckle-Kohler | Yevgeniy Puzikov | Ido Dagan | Iryna Gurevych
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results. In this work we propose an intuitive method for mapping three previously annotated corpora onto a single factuality scale, thereby enabling models to be tested across these corpora. In addition, we design a novel model for factuality prediction by first extending a previous rule-based factuality prediction system and applying it over an abstraction of dependency trees, and then using the output of this system in a supervised classifier. We show that this model outperforms previous methods on all three datasets. We make both the unified factuality corpus and our new model publicly available.

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A Simple Language Model based on PMI Matrix Approximations
Oren Melamud | Ido Dagan | Jacob Goldberger
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In this study, we introduce a new approach for learning language models by training them to estimate word-context pointwise mutual information (PMI), and then deriving the desired conditional probabilities from PMI at test time. Specifically, we show that with minor modifications to word2vec’s algorithm, we get principled language models that are closely related to the well-established Noise Contrastive Estimation (NCE) based language models. A compelling aspect of our approach is that our models are trained with the same simple negative sampling objective function that is commonly used in word2vec to learn word embeddings.

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Interactive Abstractive Summarization for Event News Tweets
Ori Shapira | Hadar Ronen | Meni Adler | Yael Amsterdamer | Judit Bar-Ilan | Ido Dagan
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present a novel interactive summarization system that is based on abstractive summarization, derived from a recent consolidated knowledge representation for multiple texts. We incorporate a couple of interaction mechanisms, providing a bullet-style summary while allowing to attain the most important information first and interactively drill down to more specific details. A usability study of our implementation, for event news tweets, suggests the utility of our approach for text exploration.

2016

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Porting an Open Information Extraction System from English to German
Tobias Falke | Gabriel Stanovsky | Iryna Gurevych | Ido Dagan
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Creating a Large Benchmark for Open Information Extraction
Gabriel Stanovsky | Ido Dagan
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Annotating and Predicting Non-Restrictive Noun Phrase Modifications
Gabriel Stanovsky | Ido Dagan
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Improving Hypernymy Detection with an Integrated Path-based and Distributional Method
Vered Shwartz | Yoav Goldberg | Ido Dagan
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Annotating Relation Inference in Context via Question Answering
Omer Levy | Ido Dagan
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Specifying and Annotating Reduced Argument Span Via QA-SRL
Gabriel Stanovsky | Ido Dagan | Meni Adler
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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The Negochat Corpus of Human-agent Negotiation Dialogues
Vasily Konovalov | Ron Artstein | Oren Melamud | Ido Dagan
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Annotated in-domain corpora are crucial to the successful development of dialogue systems of automated agents, and in particular for developing natural language understanding (NLU) components of such systems. Unfortunately, such important resources are scarce. In this work, we introduce an annotated natural language human-agent dialogue corpus in the negotiation domain. The corpus was collected using Amazon Mechanical Turk following the ‘Wizard-Of-Oz’ approach, where a ‘wizard’ human translates the participants’ natural language utterances in real time into a semantic language. Once dialogue collection was completed, utterances were annotated with intent labels by two independent annotators, achieving high inter-annotator agreement. Our initial experiments with an SVM classifier show that automatically inferring such labels from the utterances is far from trivial. We make our corpus publicly available to serve as an aid in the development of dialogue systems for negotiation agents, and suggest that analogous corpora can be created following our methodology and using our available source code. To the best of our knowledge this is the first publicly available negotiation dialogue corpus.

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Path-based vs. Distributional Information in Recognizing Lexical Semantic Relations
Vered Shwartz | Ido Dagan
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)

Recognizing various semantic relations between terms is beneficial for many NLP tasks. While path-based and distributional information sources are considered complementary for this task, the superior results the latter showed recently suggested that the former’s contribution might have become obsolete. We follow the recent success of an integrated neural method for hypernymy detection (Shwartz et al., 2016) and extend it to recognize multiple relations. The empirical results show that this method is effective in the multiclass setting as well. We further show that the path-based information source always contributes to the classification, and analyze the cases in which it mostly complements the distributional information.

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CogALex-V Shared Task: LexNET - Integrated Path-based and Distributional Method for the Identification of Semantic Relations
Vered Shwartz | Ido Dagan
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)

We present a submission to the CogALex 2016 shared task on the corpus-based identification of semantic relations, using LexNET (Shwartz and Dagan, 2016), an integrated path-based and distributional method for semantic relation classification. The reported results in the shared task bring this submission to the third place on subtask 1 (word relatedness), and the first place on subtask 2 (semantic relation classification), demonstrating the utility of integrating the complementary path-based and distributional information sources in recognizing concrete semantic relations. Combined with a common similarity measure, LexNET performs fairly good on the word relatedness task (subtask 1). The relatively low performance of LexNET and all other systems on subtask 2, however, confirms the difficulty of the semantic relation classification task, and stresses the need to develop additional methods for this task.

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context2vec: Learning Generic Context Embedding with Bidirectional LSTM
Oren Melamud | Jacob Goldberger | Ido Dagan
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

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Modeling Extractive Sentence Intersection via Subtree Entailment
Omer Levy | Ido Dagan | Gabriel Stanovsky | Judith Eckle-Kohler | Iryna Gurevych
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity. Despite its modeling power, it has received little attention because it is difficult for non-experts to annotate. We analyze 200 pairs of similar sentences and identify several underlying properties of sentence intersection. We leverage these insights to design an algorithm that decomposes the sentence intersection task into several simpler annotation tasks, facilitating the construction of a high quality dataset via crowdsourcing. We implement this approach and provide an annotated dataset of 1,764 sentence intersections.

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Adding Context to Semantic Data-Driven Paraphrasing
Vered Shwartz | Ido Dagan
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

2015

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A Simple Word Embedding Model for Lexical Substitution
Oren Melamud | Omer Levy | Ido Dagan
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

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Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus
Chaya Liebeskind | Ido Dagan
Proceedings of the 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH)

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Efficient Global Learning of Entailment Graphs
Jonathan Berant | Noga Alon | Ido Dagan | Jacob Goldberger
Computational Linguistics, Volume 41, Issue 2 - June 2015

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Multi-Level Alignments As An Extensible Representation Basis for Textual Entailment Algorithms
Tae-Gil Noh | Sebastian Padó | Vered Shwartz | Ido Dagan | Vivi Nastase | Kathrin Eichler | Lili Kotlerman | Meni Adler
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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Open IE as an Intermediate Structure for Semantic Tasks
Gabriel Stanovsky | Ido Dagan | Mausam
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Seed-Based Event Trigger Labeling: How far can event descriptions get us?
Ofer Bronstein | Ido Dagan | Qi Li | Heng Ji | Anette Frank
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Learning to Exploit Structured Resources for Lexical Inference
Vered Shwartz | Omer Levy | Ido Dagan | Jacob Goldberger
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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Modeling Word Meaning in Context with Substitute Vectors
Oren Melamud | Ido Dagan | Jacob Goldberger
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Do Supervised Distributional Methods Really Learn Lexical Inference Relations?
Omer Levy | Steffen Remus | Chris Biemann | Ido Dagan
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Improving Distributional Similarity with Lessons Learned from Word Embeddings
Omer Levy | Yoav Goldberg | Ido Dagan
Transactions of the Association for Computational Linguistics, Volume 3

Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves. Furthermore, we show that these modifications can be transferred to traditional distributional models, yielding similar gains. In contrast to prior reports, we observe mostly local or insignificant performance differences between the methods, with no global advantage to any single approach over the others.

2014

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Focused Entailment Graphs for Open IE Propositions
Omer Levy | Ido Dagan | Jacob Goldberger
Proceedings of the Eighteenth Conference on Computational Natural Language Learning

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Probabilistic Modeling of Joint-context in Distributional Similarity
Oren Melamud | Ido Dagan | Jacob Goldberger | Idan Szpektor | Deniz Yuret
Proceedings of the Eighteenth Conference on Computational Natural Language Learning

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Intermediary Semantic Representation through Proposition Structures
Gabriel Stanovsky | Jessica Ficler | Ido Dagan | Yoav Goldberg
Proceedings of the ACL 2014 Workshop on Semantic Parsing

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Proposition Knowledge Graphs
Gabriel Stanovsky | Omer Levy | Ido Dagan
Proceedings of the First AHA!-Workshop on Information Discovery in Text

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The BIUTTE Research Platform for Transformation-based Textual Entailment Recognition
Asher Stern | Ido Dagan
Linguistic Issues in Language Technology, Volume 9, 2014 - Perspectives on Semantic Representations for Textual Inference

Recent progress in research of the Recognizing Textual Entailment (RTE) task shows a constantly-increasing level of complexity in this research field. A way to avoid having this complexity becoming a barrier for researchers, especially for new-comers in the field, is to provide a freely available RTE system with a high level of flexibility and extensibility. In this paper, we introduce our RTE system, BiuTee2, and suggest it as an effective research framework for RTE. In particular, BiuTee follows the prominent transformation-based paradigm for RTE, and offers an accessible platform for research within this approach. We describe each of BiuTee’s components and point out the mechanisms and properties which directly support adaptations and integration of new components. In addition, we describe BiuTee’s visual tracing tool, which provides notable assistance for researchers in refining and “debugging” their knowledge resources and inference components.

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Recognizing Implied Predicate-Argument Relationships in Textual Inference
Asher Stern | Ido Dagan
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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The Excitement Open Platform for Textual Inferences
Bernardo Magnini | Roberto Zanoli | Ido Dagan | Kathrin Eichler | Guenter Neumann | Tae-Gil Noh | Sebastian Pado | Asher Stern | Omer Levy
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2013

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TruthTeller: Annotating Predicate Truth
Amnon Lotan | Asher Stern | Ido Dagan
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Semi-automatic Construction of Cross-period Thesaurus
Chaya Liebeskind | Ido Dagan | Jonathan Schler
Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

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Entailment graphs for text exploration
Ido Dagan | Bernardo Magnini
Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora

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A Two Level Model for Context Sensitive Inference Rules
Oren Melamud | Jonathan Berant | Ido Dagan | Jacob Goldberger | Idan Szpektor
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Using Lexical Expansion to Learn Inference Rules from Sparse Data
Oren Melamud | Ido Dagan | Jacob Goldberger | Idan Szpektor
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Recognizing Partial Textual Entailment
Omer Levy | Torsten Zesch | Ido Dagan | Iryna Gurevych
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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PLIS: a Probabilistic Lexical Inference System
Eyal Shnarch | Erel Segal-haLevi | Jacob Goldberger | Ido Dagan
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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Bridges Across the Language Divide — EU-BRIDGE Excitement: Exploring Customer Interactions through Textual EntailMENT
Ido Dagan | Bernardo Magnini | Guenter Neumann | Sebastian Pado
Proceedings of Machine Translation Summit XIV: European projects

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Excitement: Exploring Customer Interactions through Textual EntailMENT
Ido Dagan | Bernardo Magnini | Guenter Neumann | Sebastian Pado
Proceedings of Machine Translation Summit XIV: European projects

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SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge
Myroslava Dzikovska | Rodney Nielsen | Chris Brew | Claudia Leacock | Danilo Giampiccolo | Luisa Bentivogli | Peter Clark | Ido Dagan | Hoa Trang Dang
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

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UKP-BIU: Similarity and Entailment Metrics for Student Response Analysis
Omer Levy | Torsten Zesch | Ido Dagan | Iryna Gurevych
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Sentence Clustering via Projection over Term Clusters
Lili Kotlerman | Ido Dagan | Maya Gorodetsky | Ezra Daya
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Statistical Thesaurus Construction for a Morphologically Rich Language
Chaya Liebeskind | Ido Dagan | Jonathan Schler
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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A Probabilistic Lexical Model for Ranking Textual Inferences
Eyal Shnarch | Ido Dagan | Jacob Goldberger
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Learning Verb Inference Rules from Linguistically-Motivated Evidence
Hila Weisman | Jonathan Berant | Idan Szpektor | Ido Dagan
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages
Marianna Apidianaki | Ido Dagan | Jennifer Foster | Yuval Marton | Djamé Seddah | Reut Tsarfaty
Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages

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Learning Entailment Relations by Global Graph Structure Optimization
Jonathan Berant | Ido Dagan | Jacob Goldberger
Computational Linguistics, Volume 38, Issue 1 - March 2012

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Efficient Tree-based Approximation for Entailment Graph Learning
Jonathan Berant | Ido Dagan | Meni Adler | Jacob Goldberger
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Efficient Search for Transformation-based Inference
Asher Stern | Roni Stern | Ido Dagan | Ariel Felner
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Crowdsourcing Inference-Rule Evaluation
Naomi Zeichner | Jonathan Berant | Ido Dagan
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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BIUTEE: A Modular Open-Source System for Recognizing Textual Entailment
Asher Stern | Ido Dagan
Proceedings of the ACL 2012 System Demonstrations

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Entailment-based Text Exploration with Application to the Health-care Domain
Meni Adler | Jonathan Berant | Ido Dagan
Proceedings of the ACL 2012 System Demonstrations

2011

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Towards a Probabilistic Model for Lexical Entailment
Eyal Shnarch | Jacob Goldberger | Ido Dagan
Proceedings of the TextInfer 2011 Workshop on Textual Entailment

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Classification-based Contextual Preferences
Shachar Mirkin | Ido Dagan | Lili Kotlerman | Idan Szpektor
Proceedings of the TextInfer 2011 Workshop on Textual Entailment

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Global Learning of Typed Entailment Rules
Jonathan Berant | Ido Dagan | Jacob Goldberger
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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A Probabilistic Modeling Framework for Lexical Entailment
Eyal Shnarch | Jacob Goldberger | Ido Dagan
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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A Confidence Model for Syntactically-Motivated Entailment Proofs
Asher Stern | Ido Dagan
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

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A Support Tool for Deriving Domain Taxonomies from Wikipedia
Lili Kotlerman | Zemer Avital | Ido Dagan | Amnon Lotan | Ofer Weintraub
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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Assessing the Role of Discourse References in Entailment Inference
Shachar Mirkin | Ido Dagan | Sebastian Padó
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Global Learning of Focused Entailment Graphs
Jonathan Berant | Ido Dagan | Jacob Goldberger
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Generating Entailment Rules from FrameNet
Roni Ben Aharon | Idan Szpektor | Ido Dagan
Proceedings of the ACL 2010 Conference Short Papers

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Building Textual Entailment Specialized Data Sets: a Methodology for Isolating Linguistic Phenomena Relevant to Inference
Luisa Bentivogli | Elena Cabrio | Ido Dagan | Danilo Giampiccolo | Medea Lo Leggio | Bernardo Magnini
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper proposes a methodology for the creation of specialized data sets for Textual Entailment, made of monothematic Text-Hypothesis pairs (i.e. pairs in which only one linguistic phenomenon relevant to the entailment relation is highlighted and isolated). The expected benefits derive from the intuition that investigating the linguistic phenomena separately, i.e. decomposing the complexity of the TE problem, would yield an improvement in the development of specific strategies to cope with them. The annotation procedure assumes that humans have knowledge about the linguistic phenomena relevant to inference, and a classification of such phenomena both into fine grained and macro categories is suggested. We experimented with the proposed methodology over a sample of pairs taken from the RTE-5 data set, and investigated critical issues arising when entailment, contradiction or unknown pairs are considered. The result is a new resource, which can be profitably used both to advance the comprehension of the linguistic phenomena relevant to entailment judgments and to make a first step towards the creation of large-scale specialized data sets.

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A Resource for Investigating the Impact of Anaphora and Coreference on Inference.
Azad Abad | Luisa Bentivogli | Ido Dagan | Danilo Giampiccolo | Shachar Mirkin | Emanuele Pianta | Asher Stern
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Discourse phenomena play a major role in text processing tasks. However, so far relatively little study has been devoted to the relevance of discourse phenomena for inference. Therefore, an experimental study was carried out to assess the relevance of anaphora and coreference for Textual Entailment (TE), a prominent inference framework. First, the annotation of anaphoric and coreferential links in the RTE-5 Search data set was performed according to a specifically designed annotation scheme. As a result, a new data set was created where all anaphora and coreference instances in the entailing sentences which are relevant to the entailment judgment are solved and annotated.. A by-product of the annotation is a new “augmented” data set, where all the referring expressions which need to be resolved in the entailing sentences are replaced by explicit expressions. Starting from the final output of the annotation, the actual impact of discourse phenomena on inference engines was investigated, identifying the kind of operations that the systems need to apply to address discourse phenomena and trying to find direct mappings between these operation and annotation types.

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Recognising Entailment within Discourse
Shachar Mirkin | Jonathan Berant | Ido Dagan | Eyal Shnarch
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2009

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Articles: Bootstrapping Distributional Feature Vector Quality
Maayan Zhitomirsky-Geffet | Ido Dagan
Computational Linguistics, Volume 35, Number 3, September 2009

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Extracting Lexical Reference Rules from Wikipedia
Eyal Shnarch | Libby Barak | Ido Dagan
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Source-Language Entailment Modeling for Translating Unknown Terms
Shachar Mirkin | Lucia Specia | Nicola Cancedda | Ido Dagan | Marc Dymetman | Idan Szpektor
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Directional Distributional Similarity for Lexical Expansion
Lili Kotlerman | Ido Dagan | Idan Szpektor | Maayan Zhitomirsky-Geffet
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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Evaluating the Inferential Utility of Lexical-Semantic Resources
Shachar Mirkin | Ido Dagan | Eyal Shnarch
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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A Compact Forest for Scalable Inference over Entailment and Paraphrase Rules
Roy Bar-Haim | Jonathan Berant | Ido Dagan
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Text Categorization from Category Name via Lexical Reference
Libby Barak | Ido Dagan | Eyal Shnarch
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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Proceedings of the 2009 Workshop on Applied Textual Inference (TextInfer)
Chris Callison-Burch | Ido Dagan | Christopher Manning | Marco Pennacchiotti | Fabio Massimo Zanzotto
Proceedings of the 2009 Workshop on Applied Textual Inference (TextInfer)

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Augmenting WordNet-based Inference with Argument Mapping
Idan Szpektor | Ido Dagan
Proceedings of the 2009 Workshop on Applied Textual Inference (TextInfer)

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It’s time for a semantic inference engine
Ido Dagan
Proceedings of the Eight International Conference on Computational Semantics

2008

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Learning Entailment Rules for Unary Templates
Idan Szpektor | Ido Dagan
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Contextual Preferences
Idan Szpektor | Ido Dagan | Roy Bar-Haim | Jacob Goldberger
Proceedings of ACL-08: HLT

2007

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Cross Lingual and Semantic Retrieval for Cultural Heritage Appreciation
Idan Szpektor | Ido Dagan | Alon Lavie | Danny Shacham | Shuly Wintner
Proceedings of the Workshop on Language Technology for Cultural Heritage Data (LaTeCH 2007).

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Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Satoshi Sekine | Kentaro Inui | Ido Dagan | Bill Dolan | Danilo Giampiccolo | Bernardo Magnini
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

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The Third PASCAL Recognizing Textual Entailment Challenge
Danilo Giampiccolo | Bernardo Magnini | Ido Dagan | Bill Dolan
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

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Semantic Inference at the Lexical-Syntactic Level for Textual Entailment Recognition
Roy Bar-Haim | Ido Dagan | Iddo Greental | Idan Szpektor | Moshe Friedman
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

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Instance-based Evaluation of Entailment Rule Acquisition
Idan Szpektor | Eyal Shnarch | Ido Dagan
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Direct Word Sense Matching for Lexical Substitution
Ido Dagan | Oren Glickman | Alfio Gliozzo | Efrat Marmorshtein | Carlo Strapparava
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Integrating Pattern-Based and Distributional Similarity Methods for Lexical Entailment Acquisition
Shachar Mirkin | Ido Dagan | Maayan Geffet
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Semantic Similarity: What for?
Ido Dagan
Proceedings of the Workshop on Linguistic Distances

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Lexical Reference: a Semantic Matching Subtask
Oren Glickman | Eyal Shnarch | Ido Dagan
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Investigating Lexical Substitution Scoring for Subtitle Generation
Oren Glickman | Ido Dagan | Walter Daelemans | Mikaela Keller | Samy Bengio
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)

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Investigating a Generic Paraphrase-Based Approach for Relation Extraction
Lorenza Romano | Milen Kouylekov | Idan Szpektor | Ido Dagan | Alberto Lavelli
11th Conference of the European Chapter of the Association for Computational Linguistics

2005

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Investigating Unsupervised Learning for Text Categorization Bootstrapping
Alfio Gliozzo | Carlo Strapparava | Ido Dagan
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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A Generalized Framework for Revealing Analogous Themes across Related Topics
Zvika Marx | Ido Dagan | Eli Shamir
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)
Ido Dagan | Daniel Gildea
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

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Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment
Bill Dolan | Ido Dagan
Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment

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A Probabilistic Setting and Lexical Coocurrence Model for Textual Entailment
Oren Glickman | Ido Dagan
Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment

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The Distributional Inclusion Hypotheses and Lexical Entailment
Maayan Geffet | Ido Dagan
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

2004

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Scaling Web-based Acquisition of Entailment Relations
Idan Szpektor | Hristo Tanev | Ido Dagan | Bonaventura Coppola
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

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Feature Vector Quality and Distributional Similarity
Maayan Geffet | Ido Dagan
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2002

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Cross-dataset Clustering: Revealing Corresponding Themes across Multiple Corpora
Ido Dagan | Zvika Marx | Eli Shamir
COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002)

2000

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Incorporating Compositional Evidence in Memory-Based Partial Parsing
Yuval Krymolowski | Ido Dagan
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

1999

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Detecting Sub-Topic Correspondence through Bipartite Term Clustering
Zvika Marx | Ido Dagan | Eli Shamir
Unsupervised Learning in Natural Language Processing

1998

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A Memory-Based Approach to Learning Shallow Natural Language Patterns
Shlomo Argamon | Ido Dagan | Yuval Krymolowski
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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A Memory-Based Approach to Learning Shallow Natural Language Patterns
Shlomo Argamon | Ido Dagan | Yuval Krymolowski
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

1997

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Similarity-Based Methods for Word Sense Disambiguation
Ido Dagan | Lillian Lee | Fernando Pereira
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

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Mistake-Driven Learning in Text Categorization
Ido Dagan | Yael Karov | Dan Roth
Second Conference on Empirical Methods in Natural Language Processing

1996

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Minimizing Manual Annotation Cost in Supervised Training from Corpora
Sean P. Engelson | Ido Dagan
34th Annual Meeting of the Association for Computational Linguistics

1994

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Similarity-Based Estimation of Word Cooccurrence Probabilities
Ido Dagan | Fernando Pereira | Lillian Lee
32nd Annual Meeting of the Association for Computational Linguistics

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Termight: Identifying and Translating Technical Terminology
Ido Dagan | Ken Church
Fourth Conference on Applied Natural Language Processing

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Word Sense Disambiguation Using a Second Language Monolingual Corpus
Ido Dagan | Alon Itai
Computational Linguistics, Volume 20, Number 4, December 1994

1993

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Robust Bilingual Word Alignment for Machine Aided Translation
Ido Dagan | Kenneth Church | Willian Gale
Very Large Corpora: Academic and Industrial Perspectives

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Contextual Word Similarity and Estimation From Sparse Data
Ido Dagan | Shaul Marcus | Shaul Markovitch
31st Annual Meeting of the Association for Computational Linguistics

1992

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Automatic Translation of Noun Compounds
Ulrike Rackow | Ido Dagan | Ulrike Schwall
COLING 1992 Volume 4: The 14th International Conference on Computational Linguistics

1991

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Two Languages Are More Informative Than One
Ido Dagan | Alon Itai | Ulrike Schwall
29th Annual Meeting of the Association for Computational Linguistics

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Lexical Disambiguation: Sources of Information and their Statistical Realization
Ido Dagan
29th Annual Meeting of the Association for Computational Linguistics

1990

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Automatic Processing of Large Corpora for the Resolution of Anaphora References
Ido Dagan | Alon Itai
COLING 1990 Volume 3: Papers presented to the 13th International Conference on Computational Linguistics

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