Work

Inducing Example-based Semantic Frames from a Massive Amount of Verb Uses

Year: 2014

Type: conference-paper

Abstract: We present an unsupervised method for inducing semantic frames from verb uses in giga-word corpora.Our semantic frames are verb-specific example-based frames that are distinguished according to their senses.We use the Chinese Restaurant Process to automatically induce these frames from a massive amount of verb instances.In our experiments, we acquire broad-coverage semantic frames from two giga-word corpora, the larger comprising 20 billion words.Our experimental results indicate the effectiveness of our approach.

Source: Conference of the European Chapter of the Association for Computational Linguistics

DOI: 10.3115/v1/e14-1007

Authors: Daisuke Kawahara, Daniel Peterson, Octavian Popescu, Martha Palmer

Institutions: Kyoto University, University of Colorado Boulder, Fondazione Bruno Kessler

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Core Research for Evolutional Science and Technology

Award: RI: Small: A Bayesian Approach to Dynamic Lexical Resources for Flexible Language Processing