Improving Semantic Parsing Using Statistical Word Sense Disambiguation (Student Abstract)


  • Ritwik Bose University of Rochester
  • Siddharth Vashishtha University of Rochester
  • James Allen University of Rochester



A Semantic Parser generates a logical form graph from an utterance where the edges are semantic roles and nodes are word senses in an ontology that supports reasoning. The generated representation attempts to capture the full meaning of the utterance. While the process of parsing works to resolve lexical ambiguity, a number of errors in the logical forms arise from incorrectly assigned word sense determinations. This is especially true in logical and rule-based semantic parsers. Although the performance of statistical word sense disambiguation methods is superior to the word sense output of semantic parser, these systems do not produce the rich role structure or a detailed semantic representation of the sentence content. In this work, we use decisions from a statistical WSD system to inform a logical semantic parser and greatly improve semantic type assignments in the resulting logical forms.




How to Cite

Bose, R., Vashishtha, S., & Allen, J. (2020). Improving Semantic Parsing Using Statistical Word Sense Disambiguation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13757-13758.



Student Abstract Track