Shared Task System Description: Frustratingly Hard Compositionality Prediction

Anders Trærup Johannsen; Hector Martinez Alonso; Christian Rishøj; Anders Søgaard

Shared Task System Description: Frustratingly Hard Compositionality Prediction

Anders Trærup Johannsen, Hector Martinez Alonso, Christian Rishøj, Anders Søgaard

LUKKET: Center for Sprogteknologi

Abstract

We considered a wide range of features for the DiSCo 2011 shared task about compositionality prediction for word pairs, including COALS-based endocentricity scores, compositionality scores based on distributional clusters, statistics about wordnet-induced paraphrases, hyphenation, and the likelihood of long translation equivalents in other languages. Many of the features we considered correlated significantly with human compositionality scores, but in support vector regression experiments we obtained the best results using only COALS-based endocentricity scores. Our system was nevertheless the best performing system in the shared task, and average error reductions over a simple baseline in cross-validation were 13.7% for English glish and 50.1% for German.

Originalsprog	Engelsk
Titel	Proceedings of the Workshop on Distributional Semantics and Compositionality (DiSCo'2011)
Antal sider	4
Udgivelsessted	Portland, Oregon
Forlag	Association for Computational Linguistics
Publikationsdato	jun. 2011
Sider	29-32
ISBN (Trykt)	9781937284022
Status	Udgivet - jun. 2011

Citationsformater

Shared Task System Description: Frustratingly Hard Compositionality Prediction. / Johannsen, Anders Trærup; Martinez Alonso, Hector; Rishøj, Christian et al.

Proceedings of the Workshop on Distributional Semantics and Compositionality (DiSCo'2011). Portland, Oregon : Association for Computational Linguistics, 2011. s. 29-32.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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BT - Proceedings of the Workshop on Distributional Semantics and Compositionality (DiSCo'2011)

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Shared Task System Description: Frustratingly Hard Compositionality Prediction

Abstract

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