Jointly Modeling WSD and SRL with Markov Logic

Semantic role labeling (SRL) and word sense disambiguation (WSD) are two fundamental tasks in natural language processing to find a sentence-level semantic representation. To date, they have mostly been modeled in isolation. However, this approach neglects logical constraints between them. We therefore exploit some pipeline systems which verify the automatic all word sense disambiguation could help the semantic role labeling and vice versa. We further propose a Markov logic model that jointly labels semantic roles and disambiguates all word senses. By evaluating our model on the OntoNotes 3.0 data, we show that this joint approach leads to a higher performance for word sense disambiguation and semantic role labeling than those pipeline approaches.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Wanxiang Che, Ting Liu
• Keywords: COLING 2010, Computational Linguistics, Semantic Role, Sentence-level Semantic Representation, Word Sense Disambiguation