Paper: Dependency Language Models for Sentence Completion

ACL ID D13-1143
Title Dependency Language Models for Sentence Completion
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013

Sentence completion is a challenging seman- tic modeling task in which models must choose the most appropriate word from a given set to complete a sentence. Although a variety of language models have been ap- plied to this task in previous work, none of the existing approaches incorporate syntactic in- formation. In this paper we propose to tackle this task using a pair of simple language mod- els in which the probability of a sentence is estimated as the probability of the lexicalisa- tion of a given syntactic dependency tree. We apply our approach to the Microsoft Research Sentence Completion Challenge and show that it improves on n-gram language models by 8.7 percentage points, achieving the highest accu- racy reported to date apart from neural lan- guage models that are more complex an...