Word Sense Disambiguation Incorporating Lexical and Structural Semantic Information

We present results that show that incorporating lexical and structural semantic information is effective for word sense disambiguation. We evaluated the method by using precise information from a large treebank and an ontology automatically created from dictionary sentences. Exploiting rich semantic and structural information improves precision 2–3%. The most gains are seen with verbs, with an improvement of 5.7% over a model using only bag of words and n-gram features.

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Takaaki Tanaka, Francis Bond, Timothy Baldwin, Sanae Fujita, Chikara Hashimoto
• Keywords: EMNLP 2007, Information Improves Precision, Natural Language Processing, Structural Semantic Information, Word Sense Disambiguation