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A structured distributional model of sentence meaning and processing

Published online by Cambridge University Press:  31 July 2019

E. Chersoni ,
E. Santus ,
L. Pannitto ,
A. Lenci ,
P. Blache  and
C.-R. Huang
E. Chersoni*
Affiliation:
Department of Chinese and Bilingual Studies, Hong Kong Polytechnic University, Hong Kong, China
E. Santus
Affiliation:
Computer Science and Artificial Intelligence Lab, MIT, Cambridge (MA), United States
L. Pannitto
Affiliation:
Department of Philology, Literature and Linguistics, University of Pisa, Pisa, Italy
A. Lenci
Affiliation:
Department of Philology, Literature and Linguistics, University of Pisa, Pisa, Italy
P. Blache
Affiliation:
Laboratoire Parole et Langage, Aix-Marseille University, France
C.-R. Huang
Affiliation:
Department of Chinese and Bilingual Studies, Hong Kong Polytechnic University, Hong Kong, China
*
*Corresponding author. Email: emmanuelechersoni@gmail.com
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Abstract

Most compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a structured distributional model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations. The semantic representation of a sentence is a formal structure derived from discourse representation theory and containing distributional vectors. This structure is dynamically and incrementally built by integrating knowledge about events and their typical participants, as they are activated by lexical items. Event knowledge is modelled as a graph extracted from parsed corpora and encoding roles and relationships between participants that are represented as distributional vectors. SDM is grounded on extensive psycholinguistic research showing that generalized knowledge about events stored in semantic memory plays a key role in sentence comprehension.We evaluate SDMon two recently introduced compositionality data sets, and our results show that combining a simple compositionalmodel with event knowledge constantly improves performances, even with dif ferent types of word embeddings.

Keywords

distributional semanticsevent knowledgediscourse representation theoryword embeddingssentence processing
Type
Article
Information
Natural Language Engineering , Volume 25 , Issue 4 , July 2019 , pp. 483 - 502
Copyright
© Cambridge University Press 2019 

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