Radu Florian

Also published as: R. Florian


2021

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Bootstrapping Multilingual AMR with Contextual Word Alignments
Janaki Sheth | Young-Suk Lee | Ramón Fernandez Astudillo | Tahira Naseem | Radu Florian | Salim Roukos | Todd Ward
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We develop high performance multilingual Abstract Meaning Representation (AMR) systems by projecting English AMR annotations to other languages with weak supervision. We achieve this goal by bootstrapping transformer-based multilingual word embeddings, in particular those from cross-lingual RoBERTa (XLM-R large). We develop a novel technique for foreign-text-to-English AMR alignment, using the contextual word alignment between English and foreign language tokens. This word alignment is weakly supervised and relies on the contextualized XLM-R word embeddings. We achieve a highly competitive performance that surpasses the best published results for German, Italian, Spanish and Chinese.

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AMR Parsing with Action-Pointer Transformer
Jiawei Zhou | Tahira Naseem | Ramón Fernandez Astudillo | Radu Florian
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Abstract Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens. However, since graph nodes are semantically based on one or more sentence tokens, implicit alignments can be derived. Transition-based parsers operate over the sentence from left to right, capturing this inductive bias via alignments at the cost of limited expressiveness. In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments. We model the transitions as well as the pointer mechanism through straightforward modifications within a single Transformer architecture. Parser state and graph structure information are efficiently encoded using attention heads. We show that our action-pointer approach leads to increased expressiveness and attains large gains (+1.6 points) against the best transition-based AMR parser in very similar conditions. While using no graph re-categorization, our single model yields the second best Smatch score on AMR 2.0 (81.8), which is further improved to 83.4 with silver data and ensemble decoding.

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VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension
Haoyang Wen | Anthony Ferritto | Heng Ji | Radu Florian | Avi Sil
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, thereby making inference computationally inefficient for production use. In this work, we propose VAULT: a light-weight and parallel-efficient paragraph representation for MRC based on contextualized representation from long document input, trained using a new Gaussian distribution-based objective that pays close attention to the partially correct instances that are close to the ground-truth. We validate our VAULT architecture showing experimental results on two benchmark MRC datasets that require long context modeling; one Wikipedia-based (Natural Questions (NQ)) and the other on TechNotes (TechQA). VAULT can achieve comparable performance on NQ with a state-of-the-art (SOTA) complex document modeling approach while being 16 times faster, demonstrating the efficiency of our proposed model. We also demonstrate that our model can also be effectively adapted to a completely different domain – TechQA – with large improvement over a model fine-tuned on a previously published large PLM.

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IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News
Parul Awasthy | Jian Ni | Ken Barker | Radu Florian
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

In this paper, we present the event detection models and systems we have developed for Multilingual Protest News Detection - Shared Task 1 at CASE 2021. The shared task has 4 subtasks which cover event detection at different granularity levels (from document level to token level) and across multiple languages (English, Hindi, Portuguese and Spanish). To handle data from multiple languages, we use a multilingual transformer-based language model (XLM-R) as the input text encoder. We apply a variety of techniques and build several transformer-based models that perform consistently well across all the subtasks and languages. Our systems achieve an average F_1 score of 81.2. Out of thirteen subtask-language tracks, our submissions rank 1st in nine and 2nd in four tracks.

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IBM MNLP IE at CASE 2021 Task 2: NLI Reranking for Zero-Shot Text Classification
Ken Barker | Parul Awasthy | Jian Ni | Radu Florian
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

Supervised models can achieve very high accuracy for fine-grained text classification. In practice, however, training data may be abundant for some types but scarce or even non-existent for others. We propose a hybrid architecture that uses as much labeled data as available for fine-tuning classification models, while also allowing for types with little (few-shot) or no (zero-shot) labeled data. In particular, we pair a supervised text classification model with a Natural Language Inference (NLI) reranking model. The NLI reranker uses a textual representation of target types that allows it to score the strength with which a type is implied by a text, without requiring training data for the types. Experiments show that the NLI model is very sensitive to the choice of textual representation, but can be effective for classifying unseen types. It can also improve classification accuracy for the known types of an already highly accurate supervised model.

2020

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A Multilingual Reading Comprehension System for more than 100 Languages
Anthony Ferritto | Sara Rosenthal | Mihaela Bornea | Kazi Hasan | Rishav Chakravarti | Salim Roukos | Radu Florian | Avi Sil
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

This paper presents M-GAAMA, a Multilingual Question Answering architecture and demo system. This is the first multilingual machine reading comprehension (MRC) demo which is able to answer questions in over 100 languages. M-GAAMA answers questions from a given passage in the same or different language. It incorporates several existing multilingual models that can be used interchangeably in the demo such as M-BERT and XLM-R. The M-GAAMA demo also improves language accessibility by incorporating the IBM Watson machine translation widget to provide additional capabilities to the user to see an answer in their desired language. We also show how M-GAAMA can be used in downstream tasks by incorporating it into an END-TO-END-QA system using CFO (Chakravarti et al., 2019). We experiment with our system architecture on the Multi-Lingual Question Answering (MLQA) and the COVID-19 CORD (Wang et al., 2020; Tang et al., 2020) datasets to provide insights into the performance of the system.

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Towards building a Robust Industry-scale Question Answering System
Rishav Chakravarti | Anthony Ferritto | Bhavani Iyer | Lin Pan | Radu Florian | Salim Roukos | Avi Sil
Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

Industry-scale NLP systems necessitate two features. 1. Robustness: “zero-shot transfer learning” (ZSTL) performance has to be commendable and 2. Efficiency: systems have to train efficiently and respond instantaneously. In this paper, we introduce the development of a production model called GAAMA (Go Ahead Ask Me Anything) which possess the above two characteristics. For robustness, it trains on the recently introduced Natural Questions (NQ) dataset. NQ poses additional challenges over older datasets like SQuAD: (a) QA systems need to read and comprehend an entire Wikipedia article rather than a small passage, and (b) NQ does not suffer from observation bias during construction, resulting in less lexical overlap between the question and the article. GAAMA consists of Attention-over-Attention, diversity among attention heads, hierarchical transfer learning, and synthetic data augmentation while being computationally inexpensive. Building on top of the powerful BERTQA model, GAAMA provides a ∼2.0% absolute boost in F1 over the industry-scale state-of-the-art (SOTA) system on NQ. Further, we show that GAAMA transfers zero-shot to unseen real life and important domains as it yields respectable performance on two benchmarks: the BioASQ and the newly introduced CovidQA datasets.

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Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers
Yousef El-Kurdi | Hiroshi Kanayama | Efsun Sarioglu Kayi | Vittorio Castelli | Todd Ward | Radu Florian
Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

We present scalable Universal Dependency (UD) treebank synthesis techniques that exploit advances in language representation modeling which leverage vast amounts of unlabeled general-purpose multilingual text. We introduce a data augmentation technique that uses synthetic treebanks to improve production-grade parsers. The synthetic treebanks are generated using a state-of-the-art biaffine parser adapted with pretrained Transformer models, such as Multilingual BERT (M-BERT). The new parser improves LAS by up to two points on seven languages. The production models’ LAS performance improves as the augmented treebanks scale in size, surpassing performance of production models trained on originally annotated UD treebanks.

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Transition-based Parsing with Stack-Transformers
Ramón Fernandez Astudillo | Miguel Ballesteros | Tahira Naseem | Austin Blodgett | Radu Florian
Findings of the Association for Computational Linguistics: EMNLP 2020

Modeling the parser state is key to good performance in transition-based parsing. Recurrent Neural Networks considerably improved the performance of transition-based systems by modelling the global state, e.g. stack-LSTM parsers, or local state modeling of contextualized features, e.g. Bi-LSTM parsers. Given the success of Transformer architectures in recent parsing systems, this work explores modifications of the sequence-to-sequence Transformer architecture to model either global or local parser states in transition-based parsing. We show that modifications of the cross attention mechanism of the Transformer considerably strengthen performance both on dependency and Abstract Meaning Representation (AMR) parsing tasks, particularly for smaller models or limited training data.

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Pushing the Limits of AMR Parsing with Self-Learning
Young-Suk Lee | Ramón Fernandez Astudillo | Tahira Naseem | Revanth Gangi Reddy | Radu Florian | Salim Roukos
Findings of the Association for Computational Linguistics: EMNLP 2020

Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR. At the same time, self-learning techniques have helped push the performance boundaries of other natural language processing applications, such as machine translation or question answering. In this paper, we explore different ways in which trained models can be applied to improve AMR parsing performance, including generation of synthetic text and AMR annotations as well as refinement of actions oracle. We show that, without any additional human annotations, these techniques improve an already performant parser and achieve state-of-the-art results on AMR 1.0 and AMR 2.0.

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The TechQA Dataset
Vittorio Castelli | Rishav Chakravarti | Saswati Dana | Anthony Ferritto | Radu Florian | Martin Franz | Dinesh Garg | Dinesh Khandelwal | Scott McCarley | Michael McCawley | Mohamed Nasr | Lin Pan | Cezar Pendus | John Pitrelli | Saurabh Pujar | Salim Roukos | Andrzej Sakrajda | Avi Sil | Rosario Uceda-Sosa | Todd Ward | Rong Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We introduce TECHQA, a domain-adaptation question answering dataset for the technical support domain. The TECHQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size – 600 training, 310 dev, and 490 evaluation question/answer pairs – thus reflecting the cost of creating large labeled datasets with actual data. Hence, TECHQA is meant to stimulate research in domain adaptation rather than as a resource to build QA systems from scratch. TECHQA was obtained by crawling the IBMDeveloper and DeveloperWorks forums for questions with accepted answers provided in an IBM Technote—a technical document that addresses a specific technical issue. We also release a collection of the 801,998 Technotes available on the web as of April 4, 2019 as a companion resource that can be used to learn representations of the IT domain language.

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GPT-too: A Language-Model-First Approach for AMR-to-Text Generation
Manuel Mager | Ramón Fernandez Astudillo | Tahira Naseem | Md Arafat Sultan | Young-Suk Lee | Radu Florian | Salim Roukos
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Abstract Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring. Despite the simplicity of the approach, our experimental results show these models outperform all previous techniques on the English LDC2017T10 dataset, including the recent use of transformer architectures. In addition to the standard evaluation metrics, we provide human evaluation experiments that further substantiate the strength of our approach.

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Multi-Stage Pre-training for Low-Resource Domain Adaptation
Rong Zhang | Revanth Gangi Reddy | Md Arafat Sultan | Vittorio Castelli | Anthony Ferritto | Radu Florian | Efsun Sarioglu Kayi | Salim Roukos | Avi Sil | Todd Ward
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Transfer learning techniques are particularly useful for NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain. Current approaches directly adapt a pretrained language model (LM) on in-domain text before fine-tuning to downstream tasks. We show that extending the vocabulary of the LM with domain-specific terms leads to further gains. To a bigger effect, we utilize structure in the unlabeled data to create auxiliary synthetic tasks, which helps the LM transfer to downstream tasks. We apply these approaches incrementally on a pretrained Roberta-large LM and show considerable performance gain on three tasks in the IT domain: Extractive Reading Comprehension, Document Ranking and Duplicate Question Detection.

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ARES: A Reading Comprehension Ensembling Service
Anthony Ferritto | Lin Pan | Rishav Chakravarti | Salim Roukos | Radu Florian | J. William Murdock | Avi Sil
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We introduce ARES (A Reading Comprehension Ensembling Service): a novel Machine Reading Comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. While many of the top leaderboard submissions in popular MRC benchmarks such as the Stanford Question Answering Dataset (SQuAD) and Natural Questions (NQ) use model ensembles, the accompanying papers do not publish their ensembling strategies. In this work, we detail and evaluate various ensembling strategies using the NQ dataset. ARES leverages the CFO (Chakravarti et al., 2019) and ReactJS distributed frameworks to provide a scalable interactive Question Answering experience that capitalizes on the agreement (or lack thereof) between models to improve the answer visualization experience.

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Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks
Parul Awasthy | Bishwaranjan Bhattacharjee | John Kender | Radu Florian
Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing

Transfer learning is a popular technique to learn a task using less training data and fewer compute resources. However, selecting the correct source model for transfer learning is a challenging task. We demonstrate a novel predictive method that determines which existing source model would minimize error for transfer learning to a given target. This technique does not require learning for prediction, and avoids computational costs of trail-and-error. We have evaluated this technique on nine datasets across diverse domains, including newswire, user forums, air flight booking, cybersecurity news, etc. We show that it per-forms better than existing techniques such as fine-tuning over vanilla BERT, or curriculum learning over the largest dataset on top of BERT, resulting in average F1 score gains in excess of 3%. Moreover, our technique consistently selects the best model using fewer tries.

2019

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Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping
Jian Ni | Radu Florian
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-of-the-art RE models require large amounts of manually annotated data and language-specific resources to achieve high accuracy, it is very challenging to transfer an RE model of a resource-rich language to a resource-poor language. In this paper, we propose a new approach for cross-lingual RE model transfer based on bilingual word embedding mapping. It projects word embeddings from a target language to a source language, so that a well-trained source-language neural network RE model can be directly applied to the target language. Experiment results show that the proposed approach achieves very good performance for a number of target languages on both in-house and open datasets, using a small bilingual dictionary with only 1K word pairs.

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CFO: A Framework for Building Production NLP Systems
Rishav Chakravarti | Cezar Pendus | Andrzej Sakrajda | Anthony Ferritto | Lin Pan | Michael Glass | Vittorio Castelli | J. William Murdock | Radu Florian | Salim Roukos | Avi Sil
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

This paper introduces a novel orchestration framework, called CFO (Computation Flow Orchestrator), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments. We then demonstrate a question answering system built using this framework which incorporates state-of-the-art BERT based MRC (Machine Reading Com- prehension) with IR components to enable end-to-end answer retrieval. Results from the demo system are shown to be high quality in both academic and industry domain specific settings. Finally, we discuss best practices when (pre-)training BERT based MRC models for production systems. Screencast links: - Short video (< 3 min): http: //ibm.biz/gaama_demo - Supplementary long video (< 13 min): http://ibm.biz/gaama_cfo_demo

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Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Tahira Naseem | Abhishek Shah | Hui Wan | Radu Florian | Salim Roukos | Miguel Ballesteros
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments with an attention mechanism and we supplemented the parser with pre-processed concept identification, named entities and contextualized embeddings. We achieve a highly competitive performance that is comparable to the best published results. We show an in-depth study ablating each of the new components of the parser.

2018

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Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking
Gourab Kundu | Avi Sil | Radu Florian | Wael Hamza
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We propose an entity-centric neural crosslingual coreference model that builds on multi-lingual embeddings and language independent features. We perform both intrinsic and extrinsic evaluations of our model. In the intrinsic evaluation, we show that our model, when trained on English and tested on Chinese and Spanish, achieves competitive results to the models trained directly on Chinese and Spanish respectively. In the extrinsic evaluation, we show that our English model helps achieve superior entity linking accuracy on Chinese and Spanish test sets than the top 2015 TAC system without using any annotated data from Chinese or Spanish.

2017

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Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
Jian Ni | Georgiana Dinu | Radu Florian
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language. In this paper, we present two weakly supervised approaches for cross-lingual NER with no human annotation in a target language. The first approach is to create automatically labeled NER data for a target language via annotation projection on comparable corpora, where we develop a heuristic scheme that effectively selects good-quality projection-labeled data from noisy data. The second approach is to project distributed representations of words (word embeddings) from a target language to a source language, so that the source-language NER system can be applied to the target language without re-training. We also design two co-decoding schemes that effectively combine the outputs of the two projection-based approaches. We evaluate the performance of the proposed approaches on both in-house and open NER data for several target languages. The results show that the combined systems outperform three other weakly supervised approaches on the CoNLL data.

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Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures
Lifu Huang | Avirup Sil | Heng Ji | Radu Florian
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities_of_residence) for a given entity from a large collection of source documents. In this paper we propose an effective DNN architecture for SF with the following new strategies: (1). Take a regularized dependency graph instead of a raw sentence as input to DNN, to compress the wide contexts between query and candidate filler; (2). Incorporate two attention mechanisms: local attention learned from query and candidate filler, and global attention learned from external knowledge bases, to guide the model to better select indicative contexts to determine slot type. Experiments show that this framework outperforms state-of-the-art on both relation extraction (16% absolute F-score gain) and slot filling validation for each individual system (up to 8.5% absolute F-score gain).

2016

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Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping
Jian Ni | Radu Florian
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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One for All: Towards Language Independent Named Entity Linking
Avirup Sil | Radu Florian
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Joint Model for Answer Sentence Ranking and Answer Extraction
Md Arafat Sultan | Vittorio Castelli | Radu Florian
Transactions of the Association for Computational Linguistics, Volume 4

Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.

2015

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Shallow Training is cheap but is it good enough? Experiments with Medical Fact Coding
Ramesh Nallapati | Radu Florian
Proceedings of BioNLP 15

2013

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Finding What Matters in Questions
Xiaoqiang Luo | Hema Raghavan | Vittorio Castelli | Sameer Maskey | Radu Florian
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
Lu Wang | Hema Raghavan | Vittorio Castelli | Radu Florian | Claire Cardie
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Tutorial Abstracts at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Radu Florian | Jacob Eisenstein
Tutorial Abstracts at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2010

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Improving Mention Detection Robustness to Noisy Input
Radu Florian | John Pitrelli | Salim Roukos | Imed Zitouni
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Learning to Predict Readability using Diverse Linguistic Features
Rohit Kate | Xiaoqiang Luo | Siddharth Patwardhan | Martin Franz | Radu Florian | Raymond Mooney | Salim Roukos | Chris Welty
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2009

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Improving Coreference Resolution by Using Conversational Metadata
Xiaoqiang Luo | Radu Florian | Todd Ward
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Mention Detection Crossing the Language Barrier
Imed Zitouni | Radu Florian
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2006

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Factorizing Complex Models: A Case Study in Mention Detection
Radu Florian | Hongyan Jing | Nanda Kambhatla | Imed Zitouni
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

2005

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The Impact of Morphological Stemming on Arabic Mention Detection and Coreference Resolution
Imed Zitouni | Jeffrey Sorensen | Xiaoqiang Luo | Radu Florian
Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages

2004

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A Statistical Model for Multilingual Entity Detection and Tracking
R. Florian | H. Hassan | A. Ittycheriah | H. Jing | N. Kambhatla | X. Luo | N. Nicolov | S. Roukos
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

2003

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TIPS: A Translingual Information Processing System
Yaser Al-Onaizan | Radu Florian | Martin Franz | Hany Hassan | Young-Suk Lee | J. Scott McCarley | Kishore Papineni | Salim Roukos | Jeffrey Sorensen | Christoph Tillmann | Todd Ward | Fei Xia
Companion Volume of the Proceedings of HLT-NAACL 2003 - Demonstrations

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Named Entity Recognition through Classifier Combination
Radu Florian | Abe Ittycheriah | Hongyan Jing | Tong Zhang
Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003

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HowtogetaChineseName(Entity): Segmentation and Combination Issues
Hongyan Jing | Radu Florian | Xiaoqiang Luo | Tong Zhang | Abraham Ittycheriah
Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing

2002

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Unsupervised Italian Word Sense Disambiguation using WordNets and Unlabeled Corpora
Radu Florian | Richard Wicentowski
Proceedings of the ACL-02 Workshop on Word Sense Disambiguation: Recent Successes and Future Directions

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Modeling Consensus: Classifier Combination for Word Sense Disambiguation
Radu Florian | David Yarowsky
Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)

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Named Entity Recognition as a House of Cards: Classifier Stacking
Radu Florian
COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002)

2001

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Transformation Based Learning in the Fast Lane
Grace Ngai | Radu Florian
Second Meeting of the North American Chapter of the Association for Computational Linguistics

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Multidimensional transformation-based learning
Radu Florian | Grace Ngai
Proceedings of the ACL 2001 Workshop on Computational Natural Language Learning (ConLL)

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The John Hopkins SENSEVAL-2 System Descriptions
David Yarowsky | Silviu Cucerzan | Radu Florian | Charles Schafer | Richard Wicentowski
Proceedings of SENSEVAL-2 Second International Workshop on Evaluating Word Sense Disambiguation Systems

2000

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Coaxing Confidences from an Old Freind: Probabilistic Classifications from Transformation Rule Lists
Radu Florian | John C. Henderson | Grace Ngai
2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

1999

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Taking the load off the conference chairs-towards a digital paper-routing assistant
David Yarowsky | Radu Florian
1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

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Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation
Radu Florian | David Yarowsky
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics

1998

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Beyond N-Grams: Can Linguistic Sophistication Improve Language Modeling?
Eric Brill | Radu Florian | John C. Henderson | Lidia Mangu
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Beyond N -Grams: Can Linguistic Sophistication Improve Language Modeling?
Eric Brill | Radu Florian | John C. Henderson | Lidia Mangu
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1