Graeme Hirst


2021

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An Evaluation of Disentangled Representation Learning for Texts
Krishnapriya Vishnubhotla | Graeme Hirst | Frank Rudzicz
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Knowledge Graphs meet Moral Values
Ioana Hulpuș | Jonathan Kobbe | Heiner Stuckenschmidt | Graeme Hirst
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

Operationalizing morality is crucial for understanding multiple aspects of society that have moral values at their core – such as riots, mobilizing movements, public debates, etc. Moral Foundations Theory (MFT) has become one of the most adopted theories of morality partly due to its accompanying lexicon, the Moral Foundation Dictionary (MFD), which offers a base for computationally dealing with morality. In this work, we exploit the MFD in a novel direction by investigating how well moral values are captured by KGs. We explore three widely used KGs, and provide concept-level analogues for the MFD. Furthermore, we propose several Personalized PageRank variations in order to score all the concepts and entities in the KGs with respect to their relevance to the different moral values. Our promising results help to progress the operationalization of morality in both NLP and KG communities.

2019

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Text-based inference of moral sentiment change
Jing Yi Xie | Renato Ferreira Pinto Junior | Graeme Hirst | Yang Xu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora. Our framework is based on the premise that language use can inform people’s moral perception toward right or wrong, and we build our methodology by exploring moral biases learned from diachronic word embeddings. We demonstrate how a parameter-free model supports inference of historical shifts in moral sentiment toward concepts such as slavery and democracy over centuries at three incremental levels: moral relevance, moral polarity, and fine-grained moral dimensions. We apply this methodology to visualizing moral time courses of individual concepts and analyzing the relations between psycholinguistic variables and rates of moral sentiment change at scale. Our work offers opportunities for applying natural language processing toward characterizing moral sentiment change in society.

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Are Fictional Voices Distinguishable? Classifying Character Voices in Modern Drama
Krishnapriya Vishnubhotla | Adam Hammond | Graeme Hirst
Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

According to the literary theory of Mikhail Bakhtin, a dialogic novel is one in which characters speak in their own distinct voices, rather than serving as mouthpieces for their authors. We use text classification to determine which authors best achieve dialogism, looking at a corpus of plays from the late nineteenth and early twentieth centuries. We find that the SAGE model of text generation, which highlights deviations from a background lexical distribution, is an effective method of weighting the words of characters’ utterances. Our results show that it is indeed possible to distinguish characters by their speech in the plays of canonical writers such as George Bernard Shaw, whereas characters are clustered more closely in the works of lesser-known playwrights.

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Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?
Zhaodong Yan | Serena Jeblee | Graeme Hirst
Proceedings of the 18th BioNLP Workshop and Shared Task

We present two models for combining word and character embeddings for cause-of-death classification of verbal autopsy reports using the text of the narratives. We find that for smaller datasets (500 to 1000 records), adding character information to the model improves classification, making character-based CNNs a promising method for automated verbal autopsy coding.

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Understanding Undesirable Word Embedding Associations
Kawin Ethayarajh | David Duvenaud | Graeme Hirst
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. However, methods for measuring and removing such biases remain poorly understood. We show that for any embedding model that implicitly does matrix factorization, debiasing vectors post hoc using subspace projection (Bolukbasi et al., 2016) is, under certain conditions, equivalent to training on an unbiased corpus. We also prove that WEAT, the most common association test for word embeddings, systematically overestimates bias. Given that the subspace projection method is provably effective, we use it to derive a new measure of association called the relational inner product association (RIPA). Experiments with RIPA reveal that, on average, skipgram with negative sampling (SGNS) does not make most words any more gendered than they are in the training corpus. However, for gender-stereotyped words, SGNS actually amplifies the gender association in the corpus.

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Towards Understanding Linear Word Analogies
Kawin Ethayarajh | David Duvenaud | Graeme Hirst
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

A surprising property of word vectors is that word analogies can often be solved with vector arithmetic. However, it is unclear why arithmetic operators correspond to non-linear embedding models such as skip-gram with negative sampling (SGNS). We provide a formal explanation of this phenomenon without making the strong assumptions that past theories have made about the vector space and word distribution. Our theory has several implications. Past work has conjectured that linear substructures exist in vector spaces because relations can be represented as ratios; we prove that this holds for SGNS. We provide novel justification for the addition of SGNS word vectors by showing that it automatically down-weights the more frequent word, as weighting schemes do ad hoc. Lastly, we offer an information theoretic interpretation of Euclidean distance in vector spaces, justifying its use in capturing word dissimilarity.

2018

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Multi-task learning for interpretable cause of death classification using key phrase prediction
Serena Jeblee | Mireille Gomes | Graeme Hirst
Proceedings of the BioNLP 2018 workshop

We introduce a multi-task learning model for cause-of-death classification of verbal autopsy narratives that jointly learns to output interpretable key phrases. Adding these key phrases outperforms the baseline model and topic modeling features.

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Using context to identify the language of face-saving
Nona Naderi | Graeme Hirst
Proceedings of the 5th Workshop on Argument Mining

We created a corpus of utterances that attempt to save face from parliamentary debates and use it to automatically analyze the language of reputation defence. Our proposed model that incorporates information regarding threats to reputation can predict reputation defence language with high confidence. Further experiments and evaluations on different datasets show that the model is able to generalize to new utterances and can predict the language of reputation defence in a new dataset.

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Automated Fact-Checking of Claims in Argumentative Parliamentary Debates
Nona Naderi | Graeme Hirst
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

We present an automated approach to distinguish true, false, stretch, and dodge statements in questions and answers in the Canadian Parliament. We leverage the truthfulness annotations of a U.S. fact-checking corpus by training a neural net model and incorporating the prediction probabilities into our models. We find that in concert with other linguistic features, these probabilities can improve the multi-class classification results. We further show that dodge statements can be detected with an F1 measure as high as 82.57% in binary classification settings.

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Listwise temporal ordering of events in clinical notes
Serena Jeblee | Graeme Hirst
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

We present metrics for listwise temporal ordering of events in clinical notes, as well as a baseline listwise temporal ranking model that generates a timeline of events that can be used in downstream medical natural language processing tasks.

2017

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Argumentation Quality Assessment: Theory vs. Practice
Henning Wachsmuth | Nona Naderi | Ivan Habernal | Yufang Hou | Graeme Hirst | Iryna Gurevych | Benno Stein
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches. This paper studies to what extent the views match empirically. We find that most observations on quality phrased spontaneously are in fact adequately represented by theory. Even more, relative comparisons of arguments in practice correlate with absolute quality ratings based on theory. Our results clarify how the two views can learn from each other.

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Recognizing Reputation Defence Strategies in Critical Political Exchanges
Nona Naderi | Graeme Hirst
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

We propose a new task of automatically detecting reputation defence strategies in the field of computational argumentation. We cast the problem as relation classification, where given a pair of reputation threat and reputation defence, we determine the reputation defence strategy. We annotate a dataset of parliamentary questions and answers with reputation defence strategies. We then propose a model based on supervised learning to address the detection of these strategies, and report promising experimental results.

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Classifying Frames at the Sentence Level in News Articles
Nona Naderi | Graeme Hirst
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Previous approaches to generic frame classification analyze frames at the document level. Here, we propose a supervised based approach based on deep neural networks and distributional representations for classifying frames at the sentence level in news articles. We conduct our experiments on the publicly available Media Frames Corpus compiled from the U.S. Newspapers. Using (B)LSTMs and GRU networks to represent the meaning of frames, we demonstrate that our approach yields at least 14-point improvement over several baseline methods.

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Cross-Lingual Sentiment Analysis Without (Good) Translation
Mohamed Abdalla | Graeme Hirst
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it is possible to use a single linear transformation, with as few as 2000 word pairs, to capture fine-grained sentiment relationships between words in a cross-lingual setting. We apply these cross-lingual sentiment models to a diverse set of tasks to demonstrate their functionality in a non-English context. By effectively leveraging English sentiment knowledge without the need for accurate translation, we can analyze and extract features from other languages with scarce data at a very low cost, thus making sentiment and related analyses for many languages inexpensive.

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Computational Argumentation Quality Assessment in Natural Language
Henning Wachsmuth | Nona Naderi | Yufang Hou | Yonatan Bilu | Vinodkumar Prabhakaran | Tim Alberdingk Thijm | Graeme Hirst | Benno Stein
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation. While different quality dimensions have been approached in natural language processing, a common understanding of argumentation quality is still missing. This paper presents the first holistic work on computational argumentation quality in natural language. We comprehensively survey the diverse existing theories and approaches to assess logical, rhetorical, and dialectical quality dimensions, and we derive a systematic taxonomy from these. In addition, we provide a corpus with 320 arguments, annotated for all 15 dimensions in the taxonomy. Our results establish a common ground for research on computational argumentation quality assessment.

2016

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Detecting late-life depression in Alzheimer’s disease through analysis of speech and language
Kathleen C. Fraser | Frank Rudzicz | Graeme Hirst
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

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Semi-supervised and unsupervised categorization of posts in Web discussion forums using part-of-speech information and minimal features
Krish Perumal | Graeme Hirst
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2015

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Distinguishing Voices in The Waste Land using Computational Stylistics
Julian Brooke | Adam Hammond | Graeme Hirst
Linguistic Issues in Language Technology, Volume 12, 2015 - Literature Lifts up Computational Linguistics

T. S. Eliot’s poem The Waste Land is a notoriously challenging example of modernist poetry, mixing the independent viewpoints of over ten distinct characters without any clear demarcation of which voice is speaking when. In this work, we apply unsupervised techniques in computational stylistics to distinguish the particular styles of these voices, offering a computer’s perspective on longstanding debates in literary analysis. Our work includes a model for stylistic segmentation that looks for points of maximum stylistic variation, a k-means clustering model for detecting non-contiguous speech from the same voice, and a stylistic profiling approach which makes use of lexical resources built from a much larger collection of literary texts. Evaluating using an expert interpretation, we show clear progress in distinguishing the voices of The Waste Land as compared to appropriate baselines, and we also offer quantitative evidence both for and against that particular interpretation.

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GutenTag: an NLP-driven Tool for Digital Humanities Research in the Project Gutenberg Corpus
Julian Brooke | Adam Hammond | Graeme Hirst
Proceedings of the Fourth Workshop on Computational Linguistics for Literature

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Building a Lexicon of Formulaic Language for Language Learners
Julian Brooke | Adam Hammond | David Jacob | Vivian Tsang | Graeme Hirst | Fraser Shein
Proceedings of the 11th Workshop on Multiword Expressions

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Semi-Supervised Never-Ending Learning in Rhetorical Relation Identification
Erick Galani Maziero | Graeme Hirst | Thiago Alexandre Salgueiro Pardo
Proceedings of the International Conference Recent Advances in Natural Language Processing

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Encoding Distributional Semantics into Triple-Based Knowledge Ranking for Document Enrichment
Muyu Zhang | Bing Qin | Mao Zheng | Graeme Hirst | Ting Liu
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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Learning Lexical Embeddings with Syntactic and Lexicographic Knowledge
Tong Wang | Abdelrahman Mohamed | Graeme Hirst
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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Sentence segmentation of aphasic speech
Kathleen C. Fraser | Naama Ben-David | Graeme Hirst | Naida Graham | Elizabeth Rochon
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Encoding World Knowledge in the Evaluation of Local Coherence
Muyu Zhang | Vanessa Wei Feng | Bing Qin | Graeme Hirst | Ting Liu | Jingwen Huang
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Comparison of different feature sets for identification of variants in progressive aphasia
Kathleen C. Fraser | Graeme Hirst | Naida L. Graham | Jed A. Meltzer | Sandra E. Black | Elizabeth Rochon
Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

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Using statistical parsing to detect agrammatic aphasia
Kathleen C. Fraser | Graeme Hirst | Jed A. Meltzer | Jennifer E. Mack | Cynthia K. Thompson
Proceedings of BioNLP 2014

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Unsupervised Multiword Segmentation of Large Corpora using Prediction-Driven Decomposition of n-grams
Julian Brooke | Vivian Tsang | Graeme Hirst | Fraser Shein
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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The Impact of Deep Hierarchical Discourse Structures in the Evaluation of Text Coherence
Vanessa Wei Feng | Ziheng Lin | Graeme Hirst
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Supervised Ranking of Co-occurrence Profiles for Acquisition of Continuous Lexical Attributes
Julian Brooke | Graeme Hirst
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Resolving Shell Nouns
Varada Kolhatkar | Graeme Hirst
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing
Vanessa Wei Feng | Graeme Hirst
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Applying a Naive Bayes Similarity Measure to Word Sense Disambiguation
Tong Wang | Graeme Hirst
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Interpreting Anaphoric Shell Nouns using Antecedents of Cataphoric Shell Nouns as Training Data
Varada Kolhatkar | Heike Zinsmeister | Graeme Hirst
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Hybrid Models for Lexical Acquisition of Correlated Styles
Julian Brooke | Graeme Hirst
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Detecting Deceptive Opinions with Profile Compatibility
Vanessa Wei Feng | Graeme Hirst
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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A Multi-Dimensional Bayesian Approach to Lexical Style
Julian Brooke | Graeme Hirst
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Automatically Assessing Whether a Text Is Cliched, with Applications to Literary Analysis
Paul Cook | Graeme Hirst
Proceedings of the 9th Workshop on Multiword Expressions

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A Tale of Two Cultures: Bringing Literary Analysis and Computational Linguistics Together
Adam Hammond | Julian Brooke | Graeme Hirst
Proceedings of the Workshop on Computational Linguistics for Literature

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Clustering Voices in The Waste Land
Julian Brooke | Graeme Hirst | Adam Hammond
Proceedings of the Workshop on Computational Linguistics for Literature

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Using Other Learner Corpora in the 2013 NLI Shared Task
Julian Brooke | Graeme Hirst
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

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Annotating Anaphoric Shell Nouns with their Antecedents
Varada Kolhatkar | Heike Zinsmeister | Graeme Hirst
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse

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Computing Lexical Contrast
Saif M. Mohammad | Bonnie J. Dorr | Graeme Hirst | Peter D. Turney
Computational Linguistics, Volume 39, Issue 3 - September 2013

2012

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Resolving “This-issue” Anaphora
Varada Kolhatkar | Graeme Hirst
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Building Readability Lexicons with Unannotated Corpora
Julian Brooke | Vivian Tsang | David Jacob | Fraser Shein | Graeme Hirst
Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations

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Unsupervised Stylistic Segmentation of Poetry with Change Curves and Extrinsic Features
Julian Brooke | Adam Hammond | Graeme Hirst
Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature

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Measuring Interlanguage: Native Language Identification with L1-influence Metrics
Julian Brooke | Graeme Hirst
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The task of native language (L1) identification suffers from a relative paucity of useful training corpora, and standard within-corpus evaluation is often problematic due to topic bias. In this paper, we introduce a method for L1 identification in second language (L2) texts that relies only on much more plentiful L1 data, rather than the L2 texts that are traditionally used for training. In particular, we do word-by-word translation of large L1 blog corpora to create a mapping to L2 forms that are a possible result of language transfer, and then use that information for unsupervised classification. We show this method is effective in several different learner corpora, with bigram features being particularly useful.

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Robust, Lexicalized Native Language Identification
Julian Brooke | Graeme Hirst
Proceedings of COLING 2012

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Text-level Discourse Parsing with Rich Linguistic Features
Vanessa Wei Feng | Graeme Hirst
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Extending the Entity-based Coherence Model with Multiple Ranks
Vanessa Wei Feng | Graeme Hirst
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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Automatic identification of words with novel but infrequent senses
Paul Cook | Graeme Hirst
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation

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Refining the Notions of Depth and Density in WordNet-based Semantic Similarity Measures
Tong Wang | Graeme Hirst
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Predicting Word Clipping with Latent Semantic Analysis
Julian Brooke | Tong Wang | Graeme Hirst
Proceedings of 5th International Joint Conference on Natural Language Processing

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Classifying arguments by scheme
Vanessa Wei Feng | Graeme Hirst
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Near-synonym Lexical Choice in Latent Semantic Space
Tong Wang | Graeme Hirst
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Automatic Acquisition of Lexical Formality
Julian Brooke | Tong Wang | Graeme Hirst
Coling 2010: Posters

2009

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Extracting Synonyms from Dictionary Definitions
Tong Wang | Graeme Hirst
Proceedings of the International Conference RANLP-2009

2008

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Computing Word-Pair Antonymy
Saif Mohammad | Bonnie Dorr | Graeme Hirst
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

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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)

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Cross-Lingual Distributional Profiles of Concepts for Measuring Semantic Distance
Saif Mohammad | Iryna Gurevych | Graeme Hirst | Torsten Zesch
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Alexander Budanitsky | Graeme Hirst
Computational Linguistics, Volume 32, Number 1, March 2006

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Building and Using a Lexical Knowledge Base of Near-Synonym Differences
Diana Inkpen | Graeme Hirst
Computational Linguistics, Volume 32, Number 2, June 2006

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Distributional measures of concept-distance: A task-oriented evaluation
Saif Mohammad | Graeme Hirst
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Determining Word Sense Dominance Using a Thesaurus
Saif Mohammad | Graeme Hirst
11th Conference of the European Chapter of the Association for Computational Linguistics

2004

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Analysis of Semantic Classes in Medical Text for Question Answering
Yun Niu | Graeme Hirst
Proceedings of the Conference on Question Answering in Restricted Domains

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Non-Classical Lexical Semantic Relations
Jane Morris | Graeme Hirst
Proceedings of the Computational Lexical Semantics Workshop at HLT-NAACL 2004

2003

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Introduction to Non-Statistical Natural Language Processing
Graeme Hirst
Companion Volume of the Proceedings of HLT-NAACL 2003 - Tutorial Abstracts

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Answering Clinical Questions with Role Identification
Yun Niu | Graeme Hirst | Gregory McArthur | Patricia Rodriguez-Gianolli
Proceedings of the ACL 2003 Workshop on Natural Language Processing in Biomedicine

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Testing the Efficacy of Part-of-Speech Information in Word Completion
Afsaneh Fazly | Graeme Hirst
Proceedings of the 2003 EACL Workshop on Language Modeling for Text Entry Methods

2002

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Acquiring Collocations for Lexical Choice between Near-Synonyms
Diana Zaiu Inkpen | Graeme Hirst
Proceedings of the ACL-02 Workshop on Unsupervised Lexical Acquisition

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Near-Synonymy and Lexical Choice
Philip Edmonds | Graeme Hirst
Computational Linguistics, Volume 28, Number 2, June 2002

2001

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Book Reviews: Longman Grammar of Spoken and Written English
Graeme Hirst
Computational Linguistics, Volume 27, Number 1, March 2001

1999

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Book Reviews: EuroWordNet: A Multilingual Database with Lexical Semantic Networks
Graeme Hirst
Computational Linguistics, Volume 25, Number 4, December 1999

1998

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Generating Warning Instructions by Planning Accidents and Injuries
Daniel Ansari | Graeme Hirst
Natural Language Generation

1995

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A Uniform Treatment of Pragmatic Inferences in Simple and Complex Utterances and Sequences of Utterances
Daniel Marcu | Graeme Hirst
33rd Annual Meeting of the Association for Computational Linguistics

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Collaborating on Referring Expressions
Peter A. Heeman | Graeme Hirst
Computational Linguistics, Volume 21, Number 3, September 1995

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The Repair of Speech Act Misunderstandings by Abductive Inference
Susan W. McRoy | Graeme Hirst
Computational Linguistics, Volume 21, Number 4, December 1995

1993

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A Computational Theory of Goal-Directed Style in Syntax
Chrysanne DiMarco | Graeme Hirst
Computational Linguistics, Volume 19, Number 3, September 1993

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Abductive Explanation of Dialogue Misunderstandings
Susan McRoy | Graeme Hirst
Sixth Conference of the European Chapter of the Association for Computational Linguistics

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A Goal-Based Grammar of Rhetoric
Chrysanne DiMarco | Graeme Hirst | Marzena Makuta-Giluk
Intentionality and Structure in Discourse Relations

1991

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Lexical Cohesion Computed by Thesaural relations as an indicator of the structure of text
Jane Morris | Graeme Hirst
Computational Linguistics, Volume 17, Number 1, March 1991

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Does Conversation Analysis Have a Role in Computational Linguistics?
Graeme Hirst
Computational Linguistics, Volume 17, Number 2, June 1991

1990

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Book Reviews: Computational Linguistics: An International Handbook on Computer Oriented Language Research and Applications / COMPUTERLINGUISTIK: EIN INTERNATIONALES HANDBUCH ZUR COMPUTERGESTÜTZTEN SPRACHFORSCHUNG UND IHRER ANWENDUNGEN
Graeme Hirst
Computational Linguistics, Volume 16, Number 2, June 1990

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Book Reviews: A Connectionist Approach to Word Sense Disambiguation
Graeme Hirst
Computational Linguistics, Volume 16, Number 4, December 1990

1988

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Stylistic Grammars in Language Translation
Chrysanne DiMarco | Graeme Hirst
Coling Budapest 1988 Volume 1: International Conference on Computational Linguistics

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Presuppositions as Beliefs
Diane Horton | Graeme Hirst
Coling Budapest 1988 Volume 1: International Conference on Computational Linguistics

1986

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The detection and representation of ambiguities of intension and description
Brenda Fawcett | Graeme Hirst
24th Annual Meeting of the Association for Computational Linguistics

1985

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Automatic Semantic Interpretation: A Computer Model of Understanding Natural Language
Graeme Hirst
Computational Linguistics Formerly the American Journal of Computational Linguistics, Volume 11, Number 2-3, April-September 1985

1983

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A Foundation for Semantic Interpretation
Graeme Hirst
21st Annual Meeting of the Association for Computational Linguistics