Fine-Grained Semantic Conceptualization of FrameNet

Authors

  • Jin-woo Park POSTECH
  • Seung-won Hwang Yonsei University
  • Haixun Wang Facebook Inc.

DOI:

https://doi.org/10.1609/aaai.v30i1.10332

Keywords:

Knowledge base, conceptualization, selectional preference

Abstract

Understanding verbs is essential for many natural language tasks. Tothis end, large-scale lexical resources such as FrameNet have beenmanually constructed to annotate the semantics of verbs (frames) andtheir arguments (frame elements or FEs) in example sentences.Our goal is to "semantically conceptualize" example sentences by connectingFEs to knowledge base (KB) concepts.For example, connecting Employer FE to company concept in the KB enables the understanding thatany (unseen) company can also be FE examples.However, a naive adoption of existing KB conceptualization technique, focusingon scenarios of conceptualizing a few terms,cannot 1) scale to many FE instances (average of 29.7 instances for all FEs) and 2) leverage interdependence betweeninstances and concepts.We thus propose a scalable k-truss clusteringand a Markov Random Field (MRF) model leveraging interdependence betweenconcept-instance, concept-concept, and instance-instance pairs. Our extensive analysis with real-life data validates that our approachimproves not only the quality of the identified concepts for FrameNet, but alsothat of applications such as selectional preference.

Downloads

Published

2016-03-05

How to Cite

Park, J.- woo, Hwang, S.- won, & Wang, H. (2016). Fine-Grained Semantic Conceptualization of FrameNet. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10332

Issue

Section

Technical Papers: NLP and Knowledge Representation