A structured distributional model of sentence meaning and processing. (July 2019)

• Record Type: Journal Article
• Authors: Chersoni, E.; Santus, E.; Pannitto, L.; Lenci, A.; Blache, P.; Huang, C.-R.
• Is Part Of: Natural language engineering. Volume 25:Part 4(2019)
• Page Start: 483
• Page End: 502
• Publication Date: 2019-07
• Subjects: distributional semantics, -- event knowledge, -- discourse representation theory, -- word embeddings, -- sentence processing; Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324919000214
• Languages: English
• ISSNs: 1351-3249

Abstract

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.