Fabienne Martin


Towards a lexicon of event-selecting predicates for a French FactBank
Ingrid Falk | Fabienne Martin
Proceedings of the Workshop Computational Semantics Beyond Events and Roles

This paper presents ongoing work for the construction of a French FactBank and a lexicon of French event-selecting predicates (ESPs), by applying the factuality detection algorithm introduced in (Saurí and Pustejovsky, 2012). This algorithm relies on a lexicon of ESPs, specifying how these predicates influence the polarity of their embedded events. For this pilot study, we focused on French factive and implicative verbs, and capitalised on a lexical resource for the English counterparts of these verbs provided by the CLSI Group (Nairn et al., 2006; Karttunen, 2012).

Towards an Inferential Lexicon of Event Selecting Predicates for French
Ingrid Falk | Fabienne Martin
IWCS 2017 - 12th International Conference on Computational Semantics - Long papers


Aspectual Flexibility Increases with Agentivity and ConcretenessA Computational Classification Experiment on Polysemous Verbs
Ingrid Falk | Fabienne Martin
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present an experimental study making use of a machine learning approach to identify the factors that affect the aspectual value that characterizes verbs under each of their readings. The study is based on various morpho-syntactic and semantic features collected from a French lexical resource and on a gold standard aspectual classification of verb readings designed by an expert. Our results support the tested hypothesis, namely that agentivity and abstractness influence lexical aspect.

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Automatic Identification of Aspectual Classes across Verbal Readings
Ingrid Falk | Fabienne Martin
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics


Identification of Rare & Novel Senses Using Translations in a Parallel Corpus
Richard Schwarz | Hinrich Schütze | Fabienne Martin | Achim Stein
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The identification of rare and novel senses is a challenge in lexicography. In this paper, we present a new method for finding such senses using a word aligned multilingual parallel corpus. We use the Europarl corpus and therein concentrate on French verbs. We represent each occurrence of a French verb as a high dimensional term vector. The dimensions of such a vector are the possible translations of the verb according to the underlying word alignment. The dimensions are weighted by a weighting scheme to adjust to the significance of any particular translation. After collecting these vectors we apply forms of the K-means algorithm on the resulting vector space to produce clusters of distinct senses, so that standard uses produce large homogeneous clusters while rare and novel uses appear in small or heterogeneous clusters. We show in a qualitative and quantitative evaluation that the method can successfully find rare and novel senses.


Disambiguation of Polysemous Verbs for Rule-based Inferencing
Fabienne Martin | Dennis Spohr | Achim Stein
Proceedings of the Eight International Conference on Computational Semantics