Philip Resnik


2021

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Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
Nazli Goharian | Philip Resnik | Andrew Yates | Molly Ireland | Kate Niederhoffer | Rebecca Resnik
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

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Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task
Sean MacAvaney | Anjali Mittu | Glen Coppersmith | Jeff Leintz | Philip Resnik
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access

Progress on NLP for mental health — indeed, for healthcare in general — is hampered by obstacles to shared, community-level access to relevant data. We report on what is, to our knowledge, the first attempt to address this problem in mental health by conducting a shared task using sensitive data in a secure data enclave. Participating teams received access to Twitter posts donated for research, including data from users with and without suicide attempts, and did all work with the dataset entirely within a secure computational environment. We discuss the task, team results, and lessons learned to set the stage for future tasks on sensitive or confidential data.

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Syntopical Graphs for Computational Argumentation Tasks
Joe Barrow | Rajiv Jain | Nedim Lipka | Franck Dernoncourt | Vlad Morariu | Varun Manjunatha | Douglas Oard | Philip Resnik | Henning Wachsmuth
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection. We introduce a general approach to these tasks motivated by syntopical reading, a reading process that emphasizes comparing and contrasting viewpoints in order to improve topic understanding. To capture collection-level context, we introduce the syntopical graph, a data structure for linking claims within a collection. A syntopical graph is a typed multi-graph where nodes represent claims and edges represent different possible pairwise relationships, such as entailment, paraphrase, or support. Experiments applying syntopical graphs to the problems of detecting stance and aspects demonstrate state-of-the-art performance in each domain, significantly outperforming approaches that do not utilize collection-level information.

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Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension
Shohini Bhattasali | Philip Resnik
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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A Joint Model for Document Segmentation and Segment Labeling
Joe Barrow | Rajiv Jain | Vlad Morariu | Varun Manjunatha | Douglas Oard | Philip Resnik
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections. Where previous work on text segmentation considers the tasks of document segmentation and segment labeling separately, we show that the tasks contain complementary information and are best addressed jointly. We introduce Segment Pooling LSTM (S-LSTM), which is capable of jointly segmenting a document and labeling segments. In support of joint training, we develop a method for teaching the model to recover from errors by aligning the predicted and ground truth segments. We show that S-LSTM reduces segmentation error by 30% on average, while also improving segment labeling.

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A Prioritization Model for Suicidality Risk Assessment
Han-Chin Shing | Philip Resnik | Douglas Oard
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We reframe suicide risk assessment from social media as a ranking problem whose goal is maximizing detection of severely at-risk individuals given the time available. Building on measures developed for resource-bounded document retrieval, we introduce a well founded evaluation paradigm, and demonstrate using an expert-annotated test collection that meaningful improvements over plausible cascade model baselines can be achieved using an approach that jointly ranks individuals and their social media posts.

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Improving Neural Topic Models using Knowledge Distillation
Alexander Miserlis Hoyle | Pranav Goel | Philip Resnik
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our modular method can be straightforwardly applied with any neural topic model to improve topic quality, which we demonstrate using two models having disparate architectures, obtaining state-of-the-art topic coherence. We show that our adaptable framework not only improves performance in the aggregate over all estimated topics, as is commonly reported, but also in head-to-head comparisons of aligned topics.

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Developing a Curated Topic Model for COVID-19 Medical Research Literature
Philip Resnik | Katherine E. Goodman | Mike Moran
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.

2019

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A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability
Weiwei Yang | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Multilingual topic models (MTMs) learn topics on documents in multiple languages. Past models align topics across languages by implicitly assuming the documents in different languages are highly comparable, often a false assumption. We introduce a new model that does not rely on this assumption, particularly useful in important low-resource language scenarios. Our MTM learns weighted topic links and connects cross-lingual topics only when the dominant words defining them are similar, outperforming LDA and previous MTMs in classification tasks using documents’ topic posteriors as features. It also learns coherent topics on documents with low comparability.

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Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
Kate Niederhoffer | Kristy Hollingshead | Philip Resnik | Rebecca Resnik | Kate Loveys
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

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CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts
Ayah Zirikly | Philip Resnik | Özlem Uzuner | Kristy Hollingshead
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

The shared task for the 2019 Workshop on Computational Linguistics and Clinical Psychology (CLPsych’19) introduced an assessment of suicide risk based on social media postings, using data from Reddit to identify users at no, low, moderate, or severe risk. Two variations of the task focused on users whose posts to the r/SuicideWatch subreddit indicated they might be at risk; a third task looked at screening users based only on their more everyday (non-SuicideWatch) posts. We received submissions from 15 different teams, and the results provide progress and insight into the value of language signal in helping to predict risk level.

2018

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Assessing Composition in Sentence Vector Representations
Allyson Ettinger | Ahmed Elgohary | Colin Phillips | Philip Resnik
Proceedings of the 27th International Conference on Computational Linguistics

An important component of achieving language understanding is mastering the composition of sentence meaning, but an immediate challenge to solving this problem is the opacity of sentence vector representations produced by current neural sentence composition models. We present a method to address this challenge, developing tasks that directly target compositional meaning information in sentence vector representations with a high degree of precision and control. To enable the creation of these controlled tasks, we introduce a specialized sentence generation system that produces large, annotated sentence sets meeting specified syntactic, semantic and lexical constraints. We describe the details of the method and generation system, and then present results of experiments applying our method to probe for compositional information in embeddings from a number of existing sentence composition models. We find that the method is able to extract useful information about the differing capacities of these models, and we discuss the implications of our results with respect to these systems’ capturing of sentence information. We make available for public use the datasets used for these experiments, as well as the generation system.

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Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
Kate Loveys | Kate Niederhoffer | Emily Prud’hommeaux | Rebecca Resnik | Philip Resnik
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

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Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings
Han-Chin Shing | Suraj Nair | Ayah Zirikly | Meir Friedenberg | Hal Daumé III | Philip Resnik
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

We report on the creation of a dataset for studying assessment of suicide risk via online postings in Reddit. Evaluation of risk-level annotations by experts yields what is, to our knowledge, the first demonstration of reliability in risk assessment by clinicians based on social media postings. We also introduce and demonstrate the value of a new, detailed rubric for assessing suicide risk, compare crowdsourced with expert performance, and present baseline predictive modeling experiments using the new dataset, which will be made available to researchers through the American Association of Suicidology.

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CLPsych 2018 Shared Task: Predicting Current and Future Psychological Health from Childhood Essays
Veronica Lynn | Alissa Goodman | Kate Niederhoffer | Kate Loveys | Philip Resnik | H. Andrew Schwartz
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

We describe the shared task for the CLPsych 2018 workshop, which focused on predicting current and future psychological health from an essay authored in childhood. Language-based predictions of a person’s current health have the potential to supplement traditional psychological assessment such as questionnaires, improving intake risk measurement and monitoring. Predictions of future psychological health can aid with both early detection and the development of preventative care. Research into the mental health trajectory of people, beginning from their childhood, has thus far been an area of little work within the NLP community. This shared task represents one of the first attempts to evaluate the use of early language to predict future health; this has the potential to support a wide variety of clinical health care tasks, from early assessment of lifetime risk for mental health problems, to optimal timing for targeted interventions aimed at both prevention and treatment.

2017

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Adapting Topic Models using Lexical Associations with Tree Priors
Weiwei Yang | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Models work best when they are optimized taking into account the evaluation criteria that people care about. For topic models, people often care about interpretability, which can be approximated using measures of lexical association. We integrate lexical association into topic optimization using tree priors, which provide a flexible framework that can take advantage of both first order word associations and the higher-order associations captured by word embeddings. Tree priors improve topic interpretability without hurting extrinsic performance.

2016

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A Discriminative Topic Model using Document Network Structure
Weiwei Yang | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning Text Pair Similarity with Context-sensitive Autoencoders
Hadi Amiri | Philip Resnik | Jordan Boyd-Graber | Hal Daumé III
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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The UMD CLPsych 2016 Shared Task System: Text Representation for Predicting Triage of Forum Posts about Mental Health
Meir Friedenberg | Hadi Amiri | Hal Daumé III | Philip Resnik
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

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The GW/UMD CLPsych 2016 Shared Task System
Ayah Zirikly | Varun Kumar | Philip Resnik
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

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Probing for semantic evidence of composition by means of simple classification tasks
Allyson Ettinger | Ahmed Elgohary | Philip Resnik
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

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Retrofitting Sense-Specific Word Vectors Using Parallel Text
Allyson Ettinger | Philip Resnik | Marine Carpuat
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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CLIP@UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to Search
Sudha Rao | Yogarshi Vyas | Hal Daumé III | Philip Resnik
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors
Weiwei Yang | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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The University of Maryland CLPsych 2015 Shared Task System
Philip Resnik | William Armstrong | Leonardo Claudino | Thang Nguyen
Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

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Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter
Philip Resnik | William Armstrong | Leonardo Claudino | Thang Nguyen | Viet-An Nguyen | Jordan Boyd-Graber
Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

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Data Selection With Fewer Words
Amittai Axelrod | Philip Resnik | Xiaodong He | Mari Ostendorf
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress
Viet-An Nguyen | Jordan Boyd-Graber | Philip Resnik | Kristina Miler
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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The Media Frames Corpus: Annotations of Frames Across Issues
Dallas Card | Amber E. Boydstun | Justin H. Gross | Philip Resnik | Noah A. Smith
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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Dialogue focus tracking for zero pronoun resolution
Sudha Rao | Allyson Ettinger | Hal Daumé III | Philip Resnik
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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I Want to Talk About, Again, My Record On Energy ...”: Modeling Agendas and Framing in Political Debates and Other Conversations
Philip Resnik
Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science

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Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality
Philip Resnik | Rebecca Resnik | Margaret Mitchell
Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

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Sometimes Average is Best: The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling
Viet-An Nguyen | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Political Ideology Detection Using Recursive Neural Networks
Mohit Iyyer | Peter Enns | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Unified Model for Soft Linguistic Reordering Constraints in Statistical Machine Translation
Junhui Li | Yuval Marton | Philip Resnik | Hal Daumé III
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Using Topic Modeling to Improve Prediction of Neuroticism and Depression in College Students
Philip Resnik | Anderson Garron | Rebecca Resnik
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Modeling Syntactic and Semantic Structures in Hierarchical Phrase-based Translation
Junhui Li | Philip Resnik | Hal Daumé III
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Argviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations
Viet-An Nguyen | Yuening Hu | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 2013 NAACL HLT Demonstration Session

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Towards Efficient Large-Scale Feature-Rich Statistical Machine Translation
Vladimir Eidelman | Ke Wu | Ferhan Ture | Philip Resnik | Jimmy Lin
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Online Relative Margin Maximization for Statistical Machine Translation
Vladimir Eidelman | Yuval Marton | Philip Resnik
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Mr. MIRA: Open-Source Large-Margin Structured Learning on MapReduce
Vladimir Eidelman | Ke Wu | Ferhan Ture | Philip Resnik | Jimmy Lin
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2012

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Encouraging Consistent Translation Choices
Ferhan Ture | Douglas W. Oard | Philip Resnik
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations
Viet-An Nguyen | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Topic Models for Dynamic Translation Model Adaptation
Vladimir Eidelman | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Noisy SMS Machine Translation in Low-Density Languages
Vladimir Eidelman | Kristy Hollingshead | Philip Resnik
Proceedings of the Sixth Workshop on Statistical Machine Translation

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The Value of Monolingual Crowdsourcing in a Real-World Translation Scenario: Simulation using Haitian Creole Emergency SMS Messages
Chang Hu | Philip Resnik | Yakov Kronrod | Vladimir Eidelman | Olivia Buzek | Benjamin B. Bederson
Proceedings of the Sixth Workshop on Statistical Machine Translation

2010

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Generalizing Hierarchical Phrase-based Translation using Rules with Adjacent Nonterminals
Hendra Setiawan | Philip Resnik
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Context-free reordering, finite-state translation
Chris Dyer | Philip Resnik
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Shedding (a Thousand Points of) Light on Biased Language
Tae Yano | Philip Resnik | Noah A. Smith
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

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Measuring Transitivity Using Untrained Annotators
Nitin Madnani | Jordan Boyd-Graber | Philip Resnik
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

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Error Driven Paraphrase Annotation using Mechanical Turk
Olivia Buzek | Philip Resnik | Ben Bederson
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

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The University of Maryland Statistical Machine Translation System for the Fifth Workshop on Machine Translation
Vladimir Eidelman | Chris Dyer | Philip Resnik
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models
Chris Dyer | Adam Lopez | Juri Ganitkevitch | Jonathan Weese | Ferhan Ture | Phil Blunsom | Hendra Setiawan | Vladimir Eidelman | Philip Resnik
Proceedings of the ACL 2010 System Demonstrations

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Position Paper: Improving Translation via Targeted Paraphrasing
Yakov Kronrod | Philip Resnik | Olivia Buzek | Chang Hu | Alex Quinn | Ben Bederson
Proceedings of the Workshop on Collaborative Translation: technology, crowdsourcing, and the translator perspective

Targeted paraphrasing is a new approach to the problem of obtaining cost-effective, reasonable quality translation that makes use of simple and inexpensive human computations by monolingual speakers in combination with machine translation. The key insight behind the process is that it is possible to spot likely translation errors with only monolingual knowledge of the target language, and it is possible to generate alternative ways to say the same thing (i.e. paraphrases) with only monolingual knowledge of the source language. Evaluations demonstrate that this approach can yield substantial improvements in translation quality.

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Holistic Sentiment Analysis Across Languages: Multilingual Supervised Latent Dirichlet Allocation
Jordan Boyd-Graber | Philip Resnik
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Improving Translation via Targeted Paraphrasing
Philip Resnik | Olivia Buzek | Chang Hu | Yakov Kronrod | Alex Quinn | Benjamin B. Bederson
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Modeling Perspective Using Adaptor Grammars
Eric Hardisty | Jordan Boyd-Graber | Philip Resnik
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Discriminative Word Alignment with a Function Word Reordering Model
Hendra Setiawan | Chris Dyer | Philip Resnik
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Topological Ordering of Function Words in Hierarchical Phrase-based Translation
Hendra Setiawan | Min-Yen Kan | Haizhou Li | Philip Resnik
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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Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases
Yuval Marton | Chris Callison-Burch | Philip Resnik
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Estimating Semantic Distance Using Soft Semantic Constraints in Knowledge-Source – Corpus Hybrid Models
Yuval Marton | Saif Mohammad | Philip Resnik
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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More than Words: Syntactic Packaging and Implicit Sentiment
Stephan Greene | Philip Resnik
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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The University of Maryland Statistical Machine Translation System for the Fourth Workshop on Machine Translation
Chris Dyer | Hendra Setiawan | Yuval Marton | Philip Resnik
Proceedings of the Fourth Workshop on Statistical Machine Translation

2008

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Online Large-Margin Training of Syntactic and Structural Translation Features
David Chiang | Yuval Marton | Philip Resnik
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Cross-Language Parser Adaptation between Related Languages
Daniel Zeman | Philip Resnik
Proceedings of the IJCNLP-08 Workshop on NLP for Less Privileged Languages

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Are Multiple Reference Translations Necessary? Investigating the Value of Paraphrased Reference Translations in Parameter Optimization
Nitin Madnani | Philip Resnik | Bonnie J. Dorr | Richard Schwartz
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers

Most state-of-the-art statistical machine translation systems use log-linear models, which are defined in terms of hypothesis features and weights for those features. It is standard to tune the feature weights in order to maximize a translation quality metric, using held-out test sentences and their corresponding reference translations. However, obtaining reference translations is expensive. In our earlier work (Madnani et al., 2007), we introduced a new full-sentence paraphrase technique, based on English-to-English decoding with an MT system, and demonstrated that the resulting paraphrases can be used to cut the number of human reference translations needed in half. In this paper, we take the idea a step further, asking how far it is possible to get with just a single good reference translation for each item in the development set. Our analysis suggests that it is necessary to invest in four or more human translations in order to significantly improve on a single translation augmented by monolingual paraphrases.

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Soft Syntactic Constraints for Hierarchical Phrased-Based Translation
Yuval Marton | Philip Resnik
Proceedings of ACL-08: HLT

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Generalizing Word Lattice Translation
Christopher Dyer | Smaranda Muresan | Philip Resnik
Proceedings of ACL-08: HLT

2007

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Using Paraphrases for Parameter Tuning in Statistical Machine Translation
Nitin Madnani | Necip Fazil Ayan | Philip Resnik | Bonnie Dorr
Proceedings of the Second Workshop on Statistical Machine Translation

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Tor, TorMd: Distributional Profiles of Concepts for Unsupervised Word Sense Disambiguation
Saif Mohammad | Graeme Hirst | Philip Resnik
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

2006

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Word-Based Alignment, Phrase-Based Translation: What’s the Link?
Adam Lopez | Philip Resnik
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers

State-of-the-art statistical machine translation is based on alignments between phrases – sequences of words in the source and target sentences. The learning step in these systems often relies on alignments between words. It is often assumed that the quality of this word alignment is critical for translation. However, recent results suggest that the relationship between alignment quality and translation quality is weaker than previously thought. We investigate this question directly, comparing the impact of high-quality alignments with a carefully constructed set of degraded alignments. In order to tease apart various interactions, we report experiments investigating the impact of alignments on different aspects of the system. Our results confirm a weak correlation, but they also illustrate that more data and better feature engineering may be more beneficial than better alignment.

2005

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The Hiero Machine Translation System: Extensions, Evaluation, and Analysis
David Chiang | Adam Lopez | Nitin Madnani | Christof Monz | Philip Resnik | Michael Subotin
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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OCR Post-Processing for Low Density Languages
Okan Kolak | Philip Resnik
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Pattern Visualization for Machine Translation Output
Adam Lopez | Philip Resnik
Proceedings of HLT/EMNLP 2005 Interactive Demonstrations

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Improved HMM Alignment Models for Languages with Scarce Resources
Adam Lopez | Philip Resnik
Proceedings of the ACL Workshop on Building and Using Parallel Texts

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The Linguist’s Search Engine: An Overview
Philip Resnik | Aaron Elkiss
Proceedings of the ACL Interactive Poster and Demonstration Sessions

2004

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The University of Maryland Senseval-3 system descriptions
Clara Cabezas | Indrajit Bhattacharya | Philip Resnik
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

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Inducing Frame Semantic Verb Classes from WordNet and LDOCE
Rebecca Green | Bonnie J. Dorr | Philip Resnik
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

2003

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The Web as a Parallel Corpus
Philip Resnik | Noah A. Smith
Computational Linguistics, Volume 29, Number 3, September 2003: Special Issue on the Web as Corpus

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A Generative Probabilistic OCR Model for NLP Applications
Okan Kolak | William Byrne | Philip Resnik
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

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Desparately Seeking Cebuano
Douglas W. Oard | David Doermann | Bonnie Dorr | Daqing He | Philip Resnik | Amy Weinberg | William Byrne | Sanjeev Khudanpur | David Yarowsky | Anton Leuski | Philipp Koehn | Kevin Knight
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

2002

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An Unsupervised Method for Word Sense Tagging using Parallel Corpora
Mona Diab | Philip Resnik
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Evaluating Translational Correspondence using Annotation Projection
Rebecca Hwa | Philip Resnik | Amy Weinberg | Okan Kolak
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

2001

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Mapping Lexical Entries in a Verbs Database to WordNet Senses
Rebecca Green | Lisa Pearl | Bonnie J. Dorr | Philip Resnik
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

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Improved Cross-Language Retrieval using Backoff Translation
Philip Resnik | Douglas Oard | Gina Levow
Proceedings of the First International Conference on Human Language Technology Research

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Rapidly Retargetable Interactive Translingual Retrieval
Gina-Anne Levow | Douglas W. Oard | Philip Resnik
Proceedings of the First International Conference on Human Language Technology Research

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Supervised Sense Tagging using Support Vector Machines
Clara Cabezas | Philip Resnik | Jessica Stevens
Proceedings of SENSEVAL-2 Second International Workshop on Evaluating Word Sense Disambiguation Systems

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Book Reviews: Parallel Text Processing: Alignment and Use of Translation Corpora
Philip Resnik
Computational Linguistics, Volume 27, Number 4, December 2001

1999

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Mining the Web for Bilingual Text
Philip Resnik
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics

1998

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Parallel strands: a preliminary investigation into mining the Web for bilingual text
Philip Resnik
Proceedings of the Third Conference of the Association for Machine Translation in the Americas: Technical Papers

Parallel corpora are a valuable resource for machine translation, but at present their availability and utility is limited by genre- and domain-specificity, licensing restrictions, and the basic dificulty of locating parallel texts in all but the most dominant of the world’s languages. A parallel corpus resource not yet explored is the World Wide Web, which hosts an abundance of pages in parallel translation, offering a potential solution to some of these problems and unique opportunities of its own. This paper presents the necessary first step in that exploration: a method for automatically finding parallel translated documents on the Web. The technique is conceptually simple, fully language independent, and scalable, and preliminary evaluation results indicate that the method may be accurate enough to apply without human intervention.

1997

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Semi-Automatic Acquisition of Domain-Specific Translation Lexicons
Philip Resnik | I. Dan Melamed
Fifth Conference on Applied Natural Language Processing

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Selectional Preference and Sense Disambiguation
Philip Resnik
Tagging Text with Lexical Semantics: Why, What, and How?

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A Perspective on Word Sense Disambiguation Methods and Their Evaluation
Philip Resnik
Tagging Text with Lexical Semantics: Why, What, and How?

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Evaluating Automatic Semantic Taggers
Philip Resnik
Tagging Text with Lexical Semantics: Why, What, and How?

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A Language Identification Application Built on the Java Client / Server Platform
Gary Adams | Philip Resnik
From Research to Commercial Applications: Making NLP Work in Practice

1995

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Disambiguating Noun Groupings with Respect to Wordnet Senses
Philip Resnik
Third Workshop on Very Large Corpora

1994

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A Rule-Based Approach to Prepositional Phrase Attachment Disambiguation
Eric Brill | Philip Resnik
COLING 1994 Volume 2: The 15th International Conference on Computational Linguistics

1993

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Semantic Classes and Syntactic Ambiguity
Philip Resnik
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Structural Ambiguity and Conceptual Relations
Philip Resnik | Marti A. Hearst
Very Large Corpora: Academic and Industrial Perspectives

1992

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A Class-Based Approach to Lexical Discovery
Philip Resnik
30th Annual Meeting of the Association for Computational Linguistics

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Left-Corner Parsing and Psychological Plausibility
Philip Resnik
COLING 1992 Volume 1: The 14th International Conference on Computational Linguistics

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Probabilistic Tree-Adjoining Grammar as a Framework for Statistical Natural Language Processing
Philip Resnik
COLING 1992 Volume 2: The 14th International Conference on Computational Linguistics

1990

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Multiple Underlying Systems: Translating User Requests into Programs to Produce Answers
Robert J. Bobrow | Philip Resnik | Ralph M. Weischedel
28th Annual Meeting of the Association for Computational Linguistics

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