Year: 2013
Type: conference-paper
Abstract: In this paper, we address the problem of how to use semantics to improve syntactic parsing, by using a hybrid reranking method: a k-best list generated by a symbolic parser is reranked based on parse-correctness scores given by a compositional, connectionist classifier. This classifier uses a recursive neural network to construct vector representations for phrases in a candidate parse tree in order to classify it as syntactically correct or not. Tested on the WSJ23, our method achieved a statistically significant improvement of 0.20% on F-score (2% error reduction) and 0.95% on exact match, compared with the state-of-the-art Berkeley parser. This result shows that vector-based compositional semantics can be usefully applied in syntactic parsing, and demonstrates the benefits of combining the symbolic and connectionist approaches.
Authors: Le, P., Zuidema, W., Scha, R., Allauzen, A., Larochelle, H., Manning, C., Socher, R.
Institution: University of Amsterdam
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2250318451