Diana McCarthy

Also published as: Diana F. McCarthy


2020

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Spatial Multi-Arrangement for Clustering and Multi-way Similarity Dataset Construction
Olga Majewska | Diana McCarthy | Jasper van den Bosch | Nikolaus Kriegeskorte | Ivan Vulić | Anna Korhonen
Proceedings of the 12th Language Resources and Evaluation Conference

We present a novel methodology for fast bottom-up creation of large-scale semantic similarity resources to support development and evaluation of NLP systems. Our work targets verb similarity, but the methodology is equally applicable to other parts of speech. Our approach circumvents the bottleneck of slow and expensive manual development of lexical resources by leveraging semantic intuitions of native speakers and adapting a spatial multi-arrangement approach from cognitive neuroscience, used before only with visual stimuli, to lexical stimuli. Our approach critically obtains judgments of word similarity in the context of a set of related words, rather than of word pairs in isolation. We also handle lexical ambiguity as a natural consequence of a two-phase process where verbs are placed in broad semantic classes prior to the fine-grained spatial similarity judgments. Our proposed design produces a large-scale verb resource comprising 17 relatedness-based classes and a verb similarity dataset containing similarity scores for 29,721 unique verb pairs and 825 target verbs, which we release with this paper.

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Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis
Olga Majewska | Ivan Vulić | Diana McCarthy | Anna Korhonen
Proceedings of the 28th International Conference on Computational Linguistics

We present the first evaluation of the applicability of a spatial arrangement method (SpAM) to a typologically diverse language sample, and its potential to produce semantic evaluation resources to support multilingual NLP, with a focus on verb semantics. We demonstrate SpAM’s utility in allowing for quick bottom-up creation of large-scale evaluation datasets that balance cross-lingual alignment with language specificity. Starting from a shared sample of 825 English verbs, translated into Chinese, Japanese, Finnish, Polish, and Italian, we apply a two-phase annotation process which produces (i) semantic verb classes and (ii) fine-grained similarity scores for nearly 130 thousand verb pairs. We use the two types of verb data to (a) examine cross-lingual similarities and variation, and (b) evaluate the capacity of static and contextualised representation models to accurately reflect verb semantics, contrasting the performance of large language specific pretraining models with their multilingual equivalent on semantic clustering and lexical similarity, across different domains of verb meaning. We release the data from both phases as a large-scale multilingual resource, comprising 85 verb classes and nearly 130k pairwise similarity scores, offering a wealth of possibilities for further evaluation and research on multilingual verb semantics.

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Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors
Qianchu Liu | Diana McCarthy | Anna Korhonen
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

One of the most powerful features of contextualized models is their dynamic embeddings for words in context, leading to state-of-the-art representations for context-aware lexical semantics. In this paper, we present a post-processing technique that enhances these representations by learning a transformation through static anchors. Our method requires only another pre-trained model and no labeled data is needed. We show consistent improvement in a range of benchmark tasks that test contextual variations of meaning both across different usages of a word and across different words as they are used in context. We demonstrate that while the original contextual representations can be improved by another embedding space from both contextualized and static models, the static embeddings, which have lower computational requirements, provide the most gains.

2019

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Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation
Qianchu Liu | Diana McCarthy | Ivan Vulić | Anna Korhonen
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

In this paper, we present a thorough investigation on methods that align pre-trained contextualized embeddings into shared cross-lingual context-aware embedding space, providing strong reference benchmarks for future context-aware crosslingual models. We propose a novel and challenging task, Bilingual Token-level Sense Retrieval (BTSR). It specifically evaluates the accurate alignment of words with the same meaning in cross-lingual non-parallel contexts, currently not evaluated by existing tasks such as Bilingual Contextual Word Similarity and Sentence Retrieval. We show how the proposed BTSR task highlights the merits of different alignment methods. In particular, we find that using context average type-level alignment is effective in transferring monolingual contextualized embeddings cross-lingually especially in non-parallel contexts, and at the same time improves the monolingual space. Furthermore, aligning independently trained models yields better performance than aligning multilingual embeddings with shared vocabulary.

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Second-order contexts from lexical substitutes for few-shot learning of word representations
Qianchu Liu | Diana McCarthy | Anna Korhonen
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

There is a growing awareness of the need to handle rare and unseen words in word representation modelling. In this paper, we focus on few-shot learning of emerging concepts that fully exploits only a few available contexts. We introduce a substitute-based context representation technique that can be applied on an existing word embedding space. Previous context-based approaches to modelling unseen words only consider bag-of-word first-order contexts, whereas our method aggregates contexts as second-order substitutes that are produced by a sequence-aware sentence completion model. We experimented with three tasks that aim to test the modelling of emerging concepts. We found that these tasks show different emphasis on first and second order contexts, and our substitute-based method achieves superior performance on naturally-occurring contexts from corpora.

2018

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Acquiring Verb Classes Through Bottom-Up Semantic Verb Clustering
Olga Majewska | Diana McCarthy | Ivan Vulić | Anna Korhonen
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning
Andrew Bennett | Timothy Baldwin | Jey Han Lau | Diana McCarthy | Francis Bond
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Word Sense Clustering and Clusterability
Diana McCarthy | Marianna Apidianaki | Katrin Erk
Computational Linguistics, Volume 42, Issue 2 - June 2016

2014

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Learning Word Sense Distributions, Detecting Unattested Senses and Identifying Novel Senses Using Topic Models
Jey Han Lau | Paul Cook | Diana McCarthy | Spandana Gella | Timothy Baldwin
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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SemLink+: FrameNet, VerbNet and Event Ontologies
Martha Palmer | Claire Bonial | Diana McCarthy
Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore (1929-2014)

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Novel Word-sense Identification
Paul Cook | Jey Han Lau | Diana McCarthy | Timothy Baldwin
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Semantic Clustering of Pivot Paraphrases
Marianna Apidianaki | Emilia Verzeni | Diana McCarthy
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Paraphrases extracted from parallel corpora by the pivot method (Bannard and Callison-Burch, 2005) constitute a valuable resource for multilingual NLP applications. In this study, we analyse the semantics of unigram pivot paraphrases and use a graph-based sense induction approach to unveil hidden sense distinctions in the paraphrase sets. The comparison of the acquired senses to gold data from the Lexical Substitution shared task (McCarthy and Navigli, 2007) demonstrates that sense distinctions exist in the paraphrase sets and highlights the need for a disambiguation step in applications using this resource.

2013

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Diathesis alternation approximation for verb clustering
Lin Sun | Diana McCarthy | Anna Korhonen
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Measuring Word Meaning in Context
Katrin Erk | Diana McCarthy | Nicholas Gaylord
Computational Linguistics, Volume 39, Issue 3 - September 2013

2012

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The Effects of Semantic Annotations on Precision Parse Ranking
Andrew MacKinlay | Rebecca Dridan | Diana McCarthy | Timothy Baldwin
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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DSS: Text Similarity Using Lexical Alignments of Form, Distributional Semantics and Grammatical Relations
Diana McCarthy | Spandana Gella | Siva Reddy
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Unsupervised Estimation of Word Usage Similarity
Marco Lui | Timothy Baldwin | Diana McCarthy
Proceedings of the Australasian Language Technology Association Workshop 2012

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Word Sense Induction for Novel Sense Detection
Jey Han Lau | Paul Cook | Diana McCarthy | David Newman | Timothy Baldwin
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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Exemplar-Based Word-Space Model for Compositionality Detection: Shared Task System Description
Siva Reddy | Diana McCarthy | Suresh Manandhar | Spandana Gella
Proceedings of the Workshop on Distributional Semantics and Compositionality

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Predicting Thread Linking Structure by Lexical Chaining
Li Wang | Diana McCarthy | Timothy Baldwin
Proceedings of the Australasian Language Technology Association Workshop 2011

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An Empirical Study on Compositionality in Compound Nouns
Siva Reddy | Diana McCarthy | Suresh Manandhar
Proceedings of 5th International Joint Conference on Natural Language Processing

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Dynamic and Static Prototype Vectors for Semantic Composition
Siva Reddy | Ioannis Klapaftis | Diana McCarthy | Suresh Manandhar
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

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Fast Syntactic Searching in Very Large Corpora for Many Languages
Miloš Jakubíček | Adam Kilgarriff | Diana McCarthy | Pavel Rychlý
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

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SemEval-2010 Task 2: Cross-Lingual Lexical Substitution
Rada Mihalcea | Ravi Sinha | Diana McCarthy
Proceedings of the 5th International Workshop on Semantic Evaluation

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IIITH: Domain Specific Word Sense Disambiguation
Siva Reddy | Abhilash Inumella | Diana McCarthy | Mark Stevenson
Proceedings of the 5th International Workshop on Semantic Evaluation

2009

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Graded Word Sense Assignment
Katrin Erk | Diana McCarthy
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Invited Talk: Alternative Annotations of Word Usage
Diana McCarthy
Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009)

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SemEval-2010 Task 2: Cross-Lingual Lexical Substitution
Ravi Sinha | Diana McCarthy | Rada Mihalcea
Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009)

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Investigations on Word Senses and Word Usages
Katrin Erk | Diana McCarthy | Nicholas Gaylord
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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Tutorial Abstracts of ACL-IJCNLP 2009
Diana McCarthy | Chengqing Zong
Tutorial Abstracts of ACL-IJCNLP 2009

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Estimating and Exploiting the Entropy of Sense Distributions
Peng Jin | Diana McCarthy | Rob Koeling | John Carroll
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Lexical Substitution as a Framework for Multiword Evaluation
Diana McCarthy
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper we analyse data from the SemEval lexical substitution task in those cases where the annotators indicated that the target word was part of a phrase before substituting the target with a synonym. We classify the types of phrases that were provided in this way by the annotators in order to evaluate the utility of the method as a means of producing a gold-standard for multiword evaluation. Multiword evaluation is a difficult area because lexical resources are not complete and people’s judgments on multiwords vary. Whilst we do not believe lexical substitution is necessarily a panacea for multiword evaluation, we do believe it is a useful methodology because the annotator is focused on the task of substitution. Following the analysis, we make some recommendations which would make the data easier to classify.

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Gloss-Based Semantic Similarity Metrics for Predominant Sense Acquisition
Ryu Iida | Diana McCarthy | Rob Koeling
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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From Predicting Predominant Senses to Local Context for Word Sense Disambiguation
Rob Koeling | Diana McCarthy
Semantics in Text Processing. STEP 2008 Conference Proceedings

2007

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Invited talk: Evaluating Automatic Approaches for Word Meaning Discovery and Disambiguation using Lexical Substitution
Diana F. McCarthy
Proceedings of the 16th Nordic Conference of Computational Linguistics (NODALIDA 2007)

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SemEval-2007 Task 10: English Lexical Substitution Task
Diana McCarthy | Roberto Navigli
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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Sussx: WSD using Automatically Acquired Predominant Senses
Rob Koeling | Diana McCarthy
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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Book Reviews: Word Sense Disambiguation: Algorithms and Applications, edited by Eneko Agirre and Philip Edmonds
Diana McCarthy
Computational Linguistics, Volume 33, Number 2, June 2007

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Unsupervised Acquisition of Predominant Word Senses
Diana McCarthy | Rob Koeling | Julie Weeds | John Carroll
Computational Linguistics, Volume 33, Number 4, December 2007

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Detecting Compositionality of Verb-Object Combinations using Selectional Preferences
Diana McCarthy | Sriram Venkatapathy | Aravind Joshi
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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Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties
Begoña Villada Moirón | Aline Villavicencio | Diana McCarthy | Stefan Evert | Suzanne Stevenson
Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties

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Relating WordNet Senses for Word Sense Disambiguation
Diana McCarthy
Proceedings of the Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together

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11th Conference of the European Chapter of the Association for Computational Linguistics
Diana McCarthy | Shuly Wintner
11th Conference of the European Chapter of the Association for Computational Linguistics

2005

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Domain-Specific Sense Distributions and Predominant Sense Acquisition
Rob Koeling | Diana McCarthy | John Carroll
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2004

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Finding Predominant Word Senses in Untagged Text
Diana McCarthy | Rob Koeling | Julie Weeds | John Carroll
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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Characterising Measures of Lexical Distributional Similarity
Julie Weeds | David Weir | Diana McCarthy
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Automatic Identification of Infrequent Word Senses
Diana McCarthy | Rob Koeling | Julie Weeds | John Carroll
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Using automatically acquired predominant senses for Word Sense Disambiguation
Diana McCarthy | Rob Koeling | Julie Weeds | John Carroll
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

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The “Meaning” system on the English all-words task
Luís Villarejo | Lluis Màrquez | Eneko Agirre | David Martínez | Bernardo Magnini | Carlo Strapparava | Diana McCarthy | Andrés Montoyo | Armando Suárez
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

2003

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Detecting a Continuum of Compositionality in Phrasal Verbs
Diana McCarthy | Bill Keller | John Carroll
Proceedings of the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment

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Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences
Diana McCarthy | John Carroll
Computational Linguistics, Volume 29, Number 4, December 2003

2002

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Lexical Substitution as a Task for WSD Evaluation
Diana McCarthy
Proceedings of the ACL-02 Workshop on Word Sense Disambiguation: Recent Successes and Future Directions

2001

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Disambiguating Noun and Verb Senses Using Automatically Acquired Selectional Preferences
Diana McCarthy | John Carroll | Judita Preiss
Proceedings of SENSEVAL-2 Second International Workshop on Evaluating Word Sense Disambiguation Systems

2000

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Statistical Filtering and Subcategorization Frame Acquisition
Anna Korhonen | Genevieve Gorrell | Diana McCarthy
2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

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Using Semantic Preferences to Identify Verbal Participation in Role Switching Alternations
Diana McCarthy
1st Meeting of the North American Chapter of the Association for Computational Linguistics

1998

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Detecting Verbal Participation in Diathesis Alternations
Diana McCarthy | Anna Korhonen
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Detecting Verbal Participation in Diathesis Alternations
Diana McCarthy | Anna Korhonen
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

1997

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Word Sense Disambiguation for Acquisition of Selectional Preferences
Diana McCarthy
Automatic Information Extraction and Building of Lexical Semantic Resources for NLP Applications