A Structured Distributional Model of Sentence Meaning and Processing
This work addresses sentence comprehension for natural language processing, but it is incremental as it builds on existing models by adding event knowledge.
The authors tackled the problem of representing sentence meaning in compositional distributional semantic models by proposing a Structured Distributional Model (SDM) that integrates word embeddings with formal semantics and event knowledge, resulting in improved performance on compositionality datasets.
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 modeled 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 SDM on two recently introduced compositionality datasets, and our results show that combining a simple compositional model with event knowledge constantly improves performances, even with different types of word embeddings.