Yves Peirsman


2013

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Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules
Heeyoung Lee | Angel Chang | Yves Peirsman | Nathanael Chambers | Mihai Surdeanu | Dan Jurafsky
Computational Linguistics, Volume 39, Issue 4 - December 2013

2011

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Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task
Heeyoung Lee | Yves Peirsman | Angel Chang | Nathanael Chambers | Mihai Surdeanu | Dan Jurafsky
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

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Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics
Sebastian Pado | Yves Peirsman
Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics

2010

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Cross-lingual Induction of Selectional Preferences with Bilingual Vector Spaces
Yves Peirsman | Sebastian Padó
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2009

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Predicting Strong Associations on the Basis of Corpus Data
Yves Peirsman | Dirk Geeraerts
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Word Space Models of Lexical Variation
Yves Peirsman | Dirk Speelman
Proceedings of the Workshop on Geometrical Models of Natural Language Semantics

2008

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The Construction and Evaluation of Word Space Models
Yves Peirsman | Simon De Deyne | Kris Heylen | Dirk Geeraerts
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Semantic similarity is a key issue in many computational tasks. This paper goes into the development and evaluation of two common ways of automatically calculating the semantic similarity between two words. On the one hand, such methods may depend on a manually constructed thesaurus like (Euro)WordNet. Their performance is often evaluated on the basis of a very restricted set of human similarity ratings. On the other hand, corpus-based methods rely on the distribution of two words in a corpus to determine their similarity. Their performance is generally quantified through a comparison with the judgements of the first type of approach. This paper introduces a new Gold Standard of more than 5,000 human intra-category similarity judgements. We show that corpus-based methods often outperform (Euro)WordNet on this data set, and that the use of the latter as a Gold Standard for the former, is thus often far from ideal.

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Modelling Word Similarity: an Evaluation of Automatic Synonymy Extraction Algorithms.
Kris Heylen | Yves Peirsman | Dirk Geeraerts | Dirk Speelman
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Vector-based models of lexical semantics retrieve semantically related words automatically from large corpora by exploiting the property that words with a similar meaning tend to occur in similar contexts. Despite their increasing popularity, it is unclear which kind of semantic similarity they actually capture and for which kind of words. In this paper, we use three vector-based models to retrieve semantically related words for a set of Dutch nouns and we analyse whether three linguistic properties of the nouns influence the results. In particular, we compare results from a dependency-based model with those from a 1st and 2nd order bag-of-words model and we examine the effect of the nouns’ frequency, semantic speficity and semantic class. We find that all three models find more synonyms for high-frequency nouns and those belonging to abstract semantic classses. Semantic specificty does not have a clear influence.

2006

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Unsupervised approaches to metonymy recognition
Yves Peirsman
Actes de la 13ème conférence sur le Traitement Automatique des Langues Naturelles. REncontres jeunes Chercheurs en Informatique pour le Traitement Automatique des Langues

To this day, the automatic recognition of metonymies has generally been addressed with supervised approaches. However, these require the annotation of a large number of training instances and hence, hinder the development of a wide-scale metonymy recognition system. This paper investigates if this knowledge acquisition bottleneck in metonymy recognition can be resolved by the application of unsupervised learning. Although the investigated technique, Schütze’s (1998) algorithm, enjoys considerable popularity in Word Sense Disambiguation, I will show that it is not yet robust enough to tackle the specific case of metonymy recognition. In particular, I will study the influence on its performance of four variables—the type of data set, the size of the context window, the application of SVD and the type of feature selection.

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Whats in a Name? The Automatic Recognition of Metonymical Location Names
Yves Peirsman
Proceedings of the Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together

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Example-Based Metonymy Recognition for Proper Nouns
Yves Peirsman
Student Research Workshop