Vincent Ng


2021

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Conundrums in Event Coreference Resolution: Making Sense of the State of the Art
Jing Lu | Vincent Ng
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Despite recent promising results on the application of span-based models for event reference interpretation, there is a lack of understanding of what has been improved. We present an empirical analysis of a state-of-the-art span-based event reference systems with the goal of providing the general NLP audience with a better understanding of the state of the art and reference researchers with directions for future research.

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Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting
Yi Feng | Ting Wang | Chuanyi Li | Vincent Ng | Jidong Ge | Bin Luo | Yucheng Hu | Xiaopeng Zhang
Findings of the Association for Computational Linguistics: EMNLP 2021

User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e.g., green tea), identify the online users to whom the ad package should be targeted. A (ad package specific) user targeting model is typically trained using historical clickthrough data: positive instances correspond to users who have clicked on an ad in the package before, whereas negative instances correspond to users who have not clicked on any ads in the package that were displayed to them. Collecting a sufficient amount of positive training data for training an accurate user targeting model, however, is by no means trivial. This paper focuses on the development of a method for automatic augmentation of the set of positive training instances. Experimental results on two datasets, including a real-world company dataset, demonstrate the effectiveness of our proposed method.

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Bridging Resolution: Making Sense of the State of the Art
Hideo Kobayashi | Vincent Ng
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.

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Constrained Multi-Task Learning for Event Coreference Resolution
Jing Lu | Vincent Ng
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We propose a neural event coreference model in which event coreference is jointly trained with five tasks: trigger detection, entity coreference, anaphoricity determination, realis detection, and argument extraction. To guide the learning of this complex model, we incorporate cross-task consistency constraints into the learning process as soft constraints via designing penalty functions. In addition, we propose the novel idea of viewing entity coreference and event coreference as a single coreference task, which we believe is a step towards a unified model of coreference resolution. The resulting model achieves state-of-the-art results on the KBP 2017 event coreference dataset.

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Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference
Maciej Ogrodniczuk | Sameer Pradhan | Massimo Poesio | Yulia Grishina | Vincent Ng
Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference

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Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue
Sopan Khosla | Ramesh Manuvinakurike | Vincent Ng | Massimo Poesio | Michael Strube | Carolyn Rosé
Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

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The CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue
Sopan Khosla | Juntao Yu | Ramesh Manuvinakurike | Vincent Ng | Massimo Poesio | Michael Strube | Carolyn Rosé
Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

In this paper, we provide an overview of the CODI-CRAC 2021 Shared-Task: Anaphora Resolution in Dialogue. The shared task focuses on detecting anaphoric relations in different genres of conversations. Using five conversational datasets, four of which have been newly annotated with a wide range of anaphoric relations: identity, bridging references and discourse deixis, we defined multiple subtasks focusing individually on these key relations. We discuss the evaluation scripts used to assess the system performance on these subtasks, and provide a brief summary of the participating systems and the results obtained across ?? runs from 5 teams, with most submissions achieving significantly better results than our baseline methods.

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Neural Anaphora Resolution in Dialogue
Hideo Kobayashi | Shengjie Li | Vincent Ng
Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

We describe the systems that we developed for the three tracks of the CODI-CRAC 2021 shared task, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. Our team ranked second for entity coreference resolution, first for bridging resolution, and first for discourse deixis resolution.

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The CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis Resolution in Dialogue: A Cross-Team Analysis
Shengjie Li | Hideo Kobayashi | Vincent Ng
Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

The CODI-CRAC 2021 shared task is the first shared task that focuses exclusively on anaphora resolution in dialogue and provides three tracks, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. We perform a cross-task analysis of the systems that participated in the shared task in each of these tracks.

2020

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Event Coreference Resolution with Non-Local Information
Jing Lu | Vincent Ng
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

We present two extensions to a state-of-theart joint model for event coreference resolution, which involve incorporating (1) a supervised topic model for improving trigger detection by providing global context, and (2) a preprocessing module that seeks to improve event coreference by discarding unlikely candidate antecedents of an event mention using discourse contexts computed based on salient entities. The resulting model yields the best results reported to date on the KBP 2017 English and Chinese datasets.

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Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference
Maciej Ogrodniczuk | Vincent Ng | Yulia Grishina | Sameer Pradhan
Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference

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Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art
Jing Lu | Vincent Ng
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Despite the significant progress on entity coreference resolution observed in recent years, there is a general lack of understanding of what has been improved. We present an empirical analysis of state-of-the-art resolvers with the goal of providing the general NLP audience with a better understanding of the state of the art and coreference researchers with directions for future research.

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Identifying Exaggerated Language
Li Kong | Chuanyi Li | Jidong Ge | Bin Luo | Vincent Ng
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

While hyperbole is one of the most prevalent rhetorical devices, it is arguably one of the least studied devices in the figurative language processing community. We contribute to the study of hyperbole by (1) creating a corpus focusing on sentence-level hyperbole detection, (2) performing a statistical and manual analysis of our corpus, and (3) addressing the automatic hyperbole detection task.

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Bridging Resolution: A Survey of the State of the Art
Hideo Kobayashi | Vincent Ng
Proceedings of the 28th International Conference on Computational Linguistics

Bridging reference resolution is an anaphora resolution task that is arguably more challenging and less studied than entity coreference resolution. Given that significant progress has been made on coreference resolution in recent years, we believe that bridging resolution will receive increasing attention in the NLP community. Nevertheless, progress on bridging resolution is currently hampered in part by the scarcity of large annotated corpora for model training as well as the lack of standardized evaluation protocols. This paper presents a survey of the current state of research on bridging reference resolution and discusses future research directions.

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Unsupervised Argumentation Mining in Student Essays
Isaac Persing | Vincent Ng
Proceedings of the 12th Language Resources and Evaluation Conference

State-of-the-art systems for argumentation mining are supervised, thus relying on training data containing manually annotated argument components and the relationships between them. To eliminate the reliance on annotated data, we present a novel approach to unsupervised argument mining. The key idea is to bootstrap from a small set of argument components automatically identified using simple heuristics in combination with reliable contextual cues. Results on a Stab and Gurevych’s corpus of 402 essays show that our unsupervised approach rivals two supervised baselines in performance and achieves 73.5-83.7% of the performance of a state-of-the-art neural approach.

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Aspect-Based Sentiment Analysis as Fine-Grained Opinion Mining
Gerardo Ocampo Diaz | Xuanming Zhang | Vincent Ng
Proceedings of the 12th Language Resources and Evaluation Conference

We show how the general fine-grained opinion mining concepts of opinion target and opinion expression are related to aspect-based sentiment analysis (ABSA) and discuss their benefits for resource creation over popular ABSA annotation schemes. Specifically, we first discuss why opinions modeled solely in terms of (entity, aspect) pairs inadequately captures the meaning of the sentiment originally expressed by authors and how opinion expressions and opinion targets can be used to avoid the loss of information. We then design a meaning-preserving annotation scheme and apply it to two popular ABSA datasets, the 2016 SemEval ABSA Restaurant and Laptop datasets. Finally, we discuss the importance of opinion expressions and opinion targets for next-generation ABSA systems. We make our datasets publicly available for download.

2019

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Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays
Zixuan Ke | Hrishikesh Inamdar | Hui Lin | Vincent Ng
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

While the vast majority of existing work on automated essay scoring has focused on holistic scoring, researchers have recently begun work on scoring specific dimensions of essay quality. Nevertheless, progress on dimension-specific essay scoring is limited in part by the lack of annotated corpora. To facilitate advances in this area, we design a scoring rubric for scoring a core, yet unexplored dimension of persuasive essay quality, thesis strength, and annotate a corpus of essays with thesis strength scores. We additionally identify the attributes that could impact thesis strength and annotate the essays with the values of these attributes, which, when predicted by computational models, could provide further feedback to students on why her essay receives a particular thesis strength score.

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Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Kentaro Inui | Jing Jiang | Vincent Ng | Xiaojun Wan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

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Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference
Maciej Ogrodniczuk | Sameer Pradhan | Yulia Grishina | Vincent Ng
Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

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Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data
Yin Jou Huang | Jing Lu | Sadao Kurohashi | Vincent Ng
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)

Argument compatibility is a linguistic condition that is frequently incorporated into modern event coreference resolution systems. If two event mentions have incompatible arguments in any of the argument roles, they cannot be coreferent. On the other hand, if these mentions have compatible arguments, then this may be used as information towards deciding their coreferent status. One of the key challenges in leveraging argument compatibility lies in the paucity of labeled data. In this work, we propose a transfer learning framework for event coreference resolution that utilizes a large amount of unlabeled data to learn argument compatibility of event mentions. In addition, we adopt an interactive inference network based model to better capture the compatible and incompatible relations between the context words of event mentions. Our experiments on the KBP 2017 English dataset confirm the effectiveness of our model in learning argument compatibility, which in turn improves the performance of the overall event coreference model.

2018

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Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference
Massimo Poesio | Vincent Ng | Maciej Ogrodniczuk
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

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Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
Yuen-Hsien Tseng | Hsin-Hsi Chen | Vincent Ng | Mamoru Komachi
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

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Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays
Winston Carlile | Nishant Gurrapadi | Zixuan Ke | Vincent Ng
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

While argument persuasiveness is one of the most important dimensions of argumentative essay quality, it is relatively little studied in automated essay scoring research. Progress on scoring argument persuasiveness is hindered in part by the scarcity of annotated corpora. We present the first corpus of essays that are simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument’s persuasiveness. This corpus could trigger the development of novel computational models concerning argument persuasiveness that provide useful feedback to students on why their arguments are (un)persuasive in addition to how persuasive they are.

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Modeling and Prediction of Online Product Review Helpfulness: A Survey
Gerardo Ocampo Diaz | Vincent Ng
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

As the amount of free-form user-generated reviews in e-commerce websites continues to increase, there is an increasing need for automatic mechanisms that sift through the vast amounts of user reviews and identify quality content. Review helpfulness modeling is a task which studies the mechanisms that affect review helpfulness and attempts to accurately predict it. This paper provides an overview of the most relevant work in helpfulness prediction and understanding in the past decade, discusses the insights gained from said work, and provides guidelines for future research.

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Modeling Trolling in Social Media Conversations
Luis Gerardo Mojica de la Vega | Vincent Ng
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Improving Unsupervised Keyphrase Extraction using Background Knowledge
Yang Yu | Vincent Ng
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Lightly-Supervised Modeling of Argument Persuasiveness
Isaac Persing | Vincent Ng
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We propose the first lightly-supervised approach to scoring an argument’s persuasiveness. Key to our approach is the novel hypothesis that lightly-supervised persuasiveness scoring is possible by explicitly modeling the major errors that negatively impact persuasiveness. In an evaluation on a new annotated corpus of online debate arguments, our approach rivals its fully-supervised counterparts in performance by four scoring metrics when using only 10% of the available training instances.

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Joint Learning for Event Coreference Resolution
Jing Lu | Vincent Ng
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

While joint models have been developed for many NLP tasks, the vast majority of event coreference resolvers, including the top-performing resolvers competing in the recent TAC KBP 2016 Event Nugget Detection and Coreference task, are pipeline-based, where the propagation of errors from the trigger detection component to the event coreference component is a major performance limiting factor. To address this problem, we propose a model for jointly learning event coreference, trigger detection, and event anaphoricity. Our joint model is novel in its choice of tasks and its features for capturing cross-task interactions. To our knowledge, this is the first attempt to train a mention-ranking model and employ event anaphoricity for event coreference. Our model achieves the best results to date on the KBP 2016 English and Chinese datasets.

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Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)
Maciej Ogrodniczuk | Vincent Ng
Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)

2016

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Proceedings of the Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2016)
Maciej Ogrodniczuk | Vincent Ng
Proceedings of the Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2016)

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Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)
Hsin-Hsi Chen | Yuen-Hsien Tseng | Vincent Ng | Xiaofei Lu
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)

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Joint Inference for Event Coreference Resolution
Jing Lu | Deepak Venugopal | Vibhav Gogate | Vincent Ng
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Event coreference resolution is a challenging problem since it relies on several components of the information extraction pipeline that typically yield noisy outputs. We hypothesize that exploiting the inter-dependencies between these components can significantly improve the performance of an event coreference resolver, and subsequently propose a novel joint inference based event coreference resolver using Markov Logic Networks (MLNs). However, the rich features that are important for this task are typically very hard to explicitly encode as MLN formulas since they significantly increase the size of the MLN, thereby making joint inference and learning infeasible. To address this problem, we propose a novel solution where we implicitly encode rich features into our model by augmenting the MLN distribution with low dimensional unit clauses. Our approach achieves state-of-the-art results on two standard evaluation corpora.

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Chinese Zero Pronoun Resolution with Deep Neural Networks
Chen Chen | Vincent Ng
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Modeling Stance in Student Essays
Isaac Persing | Vincent Ng
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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End-to-End Argumentation Mining in Student Essays
Isaac Persing | Vincent Ng
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Event Coreference Resolution with Multi-Pass Sieves
Jing Lu | Vincent Ng
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Multi-pass sieve approaches have been successfully applied to entity coreference resolution and many other tasks in natural language processing (NLP), owing in part to the ease of designing high-precision rules for these tasks. However, the same is not true for event coreference resolution: typically lying towards the end of the standard information extraction pipeline, an event coreference resolver assumes as input the noisy outputs of its upstream components such as the trigger identification component and the entity coreference resolution component. The difficulty in designing high-precision rules makes it challenging to successfully apply a multi-pass sieve approach to event coreference resolution. In this paper, we investigate this challenge, proposing the first multi-pass sieve approach to event coreference resolution. When evaluated on the version of the KBP 2015 corpus available to the participants of EN Task 2 (Event Nugget Detection and Coreference), our approach achieves an Avg F-score of 40.32%, outperforming the best participating system by 0.67% in Avg F-score.

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Markov Logic Networks for Text Mining: A Qualitative and Empirical Comparison with Integer Linear Programming
Luis Gerardo Mojica de la Vega | Vincent Ng
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Joint inference approaches such as Integer Linear Programming (ILP) and Markov Logic Networks (MLNs) have recently been successfully applied to many natural language processing (NLP) tasks, often outperforming their pipeline counterparts. However, MLNs are arguably much less popular among NLP researchers than ILP. While NLP researchers who desire to employ these joint inference frameworks do not necessarily have to understand their theoretical underpinnings, it is imperative that they understand which of them should be applied under what circumstances. With the goal of helping NLP researchers better understand the relative strengths and weaknesses of MLNs and ILP; we will compare them along different dimensions of interest, such as expressiveness, ease of use, scalability, and performance. To our knowledge, this is the first systematic comparison of ILP and MLNs on an NLP task.

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Advanced Markov Logic Techniques for Scalable Joint Inference in NLP
Deepak Venugopal | Vibhav Gogate | Vincent Ng
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

In the early days of the statistical NLP era, many language processing tasks were tackled using the so-called pipeline architecture: the given task is broken into a series of sub-tasks such that the output of one sub-task is an input to the next sub-task in the sequence. The pipeline architecture is appealing for various reasons, including modularity, modeling convenience, and manageable computational complexity. However, it suffers from the error propagation problem: errors made in one sub-task are propagated to the next sub-task in the sequence, leading to poor accuracy on that sub-task, which in turn leads to more errors downstream. Another disadvantage associated with it is lack of feedback: errors made in a sub-task are often not corrected using knowledge uncovered while solving another sub-task down the pipeline.Realizing these weaknesses, researchers have turned to joint inference approaches in recent years. One such approach involves the use of Markov logic, which is defined as a set of weighted first-order logic formulas and, at a high level, unifies first-order logic with probabilistic graphical models. It is an ideal modeling language (knowledge representation) for compactly representing relational and uncertain knowledge in NLP. In a typical use case of MLNs in NLP, the application designer describes the background knowledge using a few first-order logic sentences and then uses software packages such as Alchemy, Tuffy, and Markov the beast to perform learning and inference (prediction) over the MLN. However, despite its obvious advantages, over the years, researchers and practitioners have found it difficult to use MLNs effectively in many NLP applications. The main reason for this is that it is hard to scale inference and learning algorithms for MLNs to large datasets and complex models, that are typical in NLP.In this tutorial, we will introduce the audience to recent advances in scaling up inference and learning in MLNs as well as new approaches to make MLNs a "black-box" for NLP applications (with only minor tuning required on the part of the user). Specifically, we will introduce attendees to a key idea that has emerged in the MLN research community over the last few years, lifted inference , which refers to inference techniques that take advantage of symmetries (e.g., synonyms), both exact and approximate, in the MLN . We will describe how these next-generation inference techniques can be used to perform effective joint inference. We will also present our new software package for inference and learning in MLNs, Alchemy 2.0, which is based on lifted inference, focusing primarily on how it can be used to scale up inference and learning in large models and datasets for applications such as semantic similarity determination, information extraction and question answering.

2015

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Recovering Traceability Links in Requirements Documents
Zeheng Li | Mingrui Chen | LiGuo Huang | Vincent Ng
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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Modeling Argument Strength in Student Essays
Isaac Persing | Vincent Ng
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Sieve-Based Entity Linking for the Biomedical Domain
Jennifer D’Souza | Vincent Ng
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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Chinese Zero Pronoun Resolution: A Joint Unsupervised Discourse-Aware Model Rivaling State-of-the-Art Resolvers
Chen Chen | Vincent Ng
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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Sieve-Based Spatial Relation Extraction with Expanding Parse Trees
Jennifer D’Souza | Vincent Ng
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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UTD: Ensemble-Based Spatial Relation Extraction
Jennifer D’Souza | Vincent Ng
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Chinese Event Coreference Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers
Chen Chen | Vincent Ng
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing
Liang-Chih Yu | Zhifang Sui | Yue Zhang | Vincent Ng
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

2014

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Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates
Kazi Saidul Hasan | Vincent Ng
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Chinese Zero Pronoun Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers
Chen Chen | Vincent Ng
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Relieving the Computational Bottleneck: Joint Inference for Event Extraction with High-Dimensional Features
Deepak Venugopal | Chen Chen | Vibhav Gogate | Vincent Ng
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Vote Prediction on Comments in Social Polls
Isaac Persing | Vincent Ng
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Ensemble-Based Medical Relation Classification
Jennifer D’Souza | Vincent Ng
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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SinoCoreferencer: An End-to-End Chinese Event Coreference Resolver
Chen Chen | Vincent Ng
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Compared to entity coreference resolution, there is a relatively small amount of work on event coreference resolution. Much work on event coreference was done for English. In fact, to our knowledge, there are no publicly available results on Chinese event coreference resolution. This paper describes the design, implementation, and evaluation of SinoCoreferencer, an end-to-end state-of-the-art ACE-style Chinese event coreference system. We have made SinoCoreferencer publicly available, in hope to facilitate the development of high-level Chinese natural language applications that can potentially benefit from event coreference information.

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Annotating Inter-Sentence Temporal Relations in Clinical Notes
Jennifer D’Souza | Vincent Ng
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Owing in part to the surge of interest in temporal relation extraction, a number of datasets manually annotated with temporal relations between event-event pairs and event-time pairs have been produced recently. However, it is not uncommon to find missing annotations in these manually annotated datasets. Many researchers attributed this problem to “annotator fatigue”. While some of these missing relations can be recovered automatically, many of them cannot. Our goals in this paper are to (1) manually annotate certain types of missing links that cannot be automatically recovered in the i2b2 Clinical Temporal Relations Challenge Corpus, one of the recently released evaluation corpora for temporal relation extraction; and (2) empirically determine the usefulness of these additional annotations. We will make our annotations publicly available, in hopes of enabling a more accurate evaluation of temporal relation extraction systems.

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Automatic Keyphrase Extraction: A Survey of the State of the Art
Kazi Saidul Hasan | Vincent Ng
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Modeling Prompt Adherence in Student Essays
Isaac Persing | Vincent Ng
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation
Sameer Pradhan | Xiaoqiang Luo | Marta Recasens | Eduard Hovy | Vincent Ng | Michael Strube
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Modeling Thesis Clarity in Student Essays
Isaac Persing | Vincent Ng
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Extra-Linguistic Constraints on Stance Recognition in Ideological Debates
Kazi Saidul Hasan | Vincent Ng
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Chinese Event Coreference Resolution: Understanding the State of the Art
Chen Chen | Vincent Ng
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Stance Classification of Ideological Debates: Data, Models, Features, and Constraints
Kazi Saidul Hasan | Vincent Ng
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Linguistically Aware Coreference Evaluation Metrics
Chen Chen | Vincent Ng
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Classifying Temporal Relations with Rich Linguistic Knowledge
Jennifer D’Souza | Vincent Ng
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Simple Yet Powerful Native Language Identification on TOEFL11
Ching-Yi Wu | Po-Hsiang Lai | Yang Liu | Vincent Ng
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

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Frame Semantics for Stance Classification
Kazi Saidul Hasan | Vincent Ng
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

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Chinese Zero Pronoun Resolution: Some Recent Advances
Chen Chen | Vincent Ng
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Combining the Best of Two Worlds: A Hybrid Approach to Multilingual Coreference Resolution
Chen Chen | Vincent Ng
Joint Conference on EMNLP and CoNLL - Shared Task

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Translation-Based Projection for Multilingual Coreference Resolution
Altaf Rahman | Vincent Ng
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Joint Modeling for Chinese Event Extraction with Rich Linguistic Features
Chen Chen | Vincent Ng
Proceedings of COLING 2012

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Chinese Noun Phrase Coreference Resolution: Insights into the State of the Art
Chen Chen | Vincent Ng
Proceedings of COLING 2012: Posters

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Predicting Stance in Ideological Debate with Rich Linguistic Knowledge
Kazi Saidul Hasan | Vincent Ng
Proceedings of COLING 2012: Posters

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Learning the Fine-Grained Information Status of Discourse Entities
Altaf Rahman | Vincent Ng
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge
Altaf Rahman | Vincent Ng
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Learning the Information Status of Noun Phrases in Spoken Dialogues
Altaf Rahman | Vincent Ng
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Syntactic Parsing for Ranking-Based Coreference Resolution
Altaf Rahman | Vincent Ng
Proceedings of 5th International Joint Conference on Natural Language Processing

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Coreference Resolution with World Knowledge
Altaf Rahman | Vincent Ng
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Supervised Noun Phrase Coreference Research: The First Fifteen Years
Vincent Ng
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Modeling Organization in Student Essays
Isaac Persing | Alan Davis | Vincent Ng
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Inducing Fine-Grained Semantic Classes via Hierarchical and Collective Classification
Altaf Rahman | Vincent Ng
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Conundrums in Unsupervised Keyphrase Extraction: Making Sense of the State-of-the-Art
Kazi Saidul Hasan | Vincent Ng
Coling 2010: Posters

2009

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Mine the Easy, Classify the Hard: A Semi-Supervised Approach to Automatic Sentiment Classification
Sajib Dasgupta | Vincent Ng
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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Semi-Supervised Cause Identification from Aviation Safety Reports
Isaac Persing | Vincent Ng
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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Graph-Cut-Based Anaphoricity Determination for Coreference Resolution
Vincent Ng
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Learning-Based Named Entity Recognition for Morphologically-Rich, Resource-Scarce Languages
Kazi Saidul Hasan | Md. Altaf ur Rahman | Vincent Ng
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Weakly Supervised Part-of-Speech Tagging for Morphologically-Rich, Resource-Scarce Languages
Kazi Saidul Hasan | Vincent Ng
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Topic-wise, Sentiment-wise, or Otherwise? Identifying the Hidden Dimension for Unsupervised Text Classification
Sajib Dasgupta | Vincent Ng
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Supervised Models for Coreference Resolution
Altaf Rahman | Vincent Ng
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Discriminative Models for Semi-Supervised Natural Language Learning
Sajib Dasgupta | Vincent Ng
Proceedings of the NAACL HLT 2009 Workshop on Semi-supervised Learning for Natural Language Processing

2008

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Book Reviews: Semisupervised Learning for Computational Linguistics by Steven Abney
Vincent Ng
Computational Linguistics, Volume 34, Number 3, September 2008

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Unsupervised Models for Coreference Resolution
Vincent Ng
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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Semantic Class Induction and Coreference Resolution
Vincent Ng
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Unsupervised Part-of-Speech Acquisition for Resource-Scarce Languages
Sajib Dasgupta | Vincent Ng
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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High-Performance, Language-Independent Morphological Segmentation
Sajib Dasgupta | Vincent Ng
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

2006

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Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews
Vincent Ng | Sajib Dasgupta | S. M. Niaz Arifin
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

2005

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Machine Learning for Coreference Resolution: From Local Classification to Global Ranking
Vincent Ng
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

2004

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Learning Noun Phrase Anaphoricity to Improve Conference Resolution: Issues in Representation and Optimization
Vincent Ng
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

2003

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Weakly Supervised Natural Language Learning Without Redundant Views
Vincent Ng | Claire Cardie
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

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Bootstrapping Coreference Classifiers with Multiple Machine Learning Algorithms
Vincent Ng | Claire Cardie
Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing

2002

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Identifying Anaphoric and Non-Anaphoric Noun Phrases to Improve Coreference Resolution
Vincent Ng | Claire Cardie
COLING 2002: The 19th International Conference on Computational Linguistics

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Improving Machine Learning Approaches to Coreference Resolution
Vincent Ng | Claire Cardie
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Combining Sample Selection and Error-Driven Pruning for Machine Learning of Coreference Rules
Vincent Ng | Claire Cardie
Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)

2001

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Multidocument Summarization via Information Extraction
Michael White | Tanya Korelsky | Claire Cardie | Vincent Ng | David Pierce | Kiri Wagstaff
Proceedings of the First International Conference on Human Language Technology Research

2000

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Examining the Role of Statistical and Linguistic Knowledge Sources in a General-Knowledge Question-Answering System
Claire Cardie | Vincent Ng | David Pierce | Chris Buckley
Sixth Applied Natural Language Processing Conference