Understanding verbs is essential for many natural language tasks. To this end, large-scale lexical resources such as FrameNet have been manually constructed to annotate the semantics of verbs (frames) and their arguments (frame elements or FEs) in example sentences. Our goal is to "semantically conceptualize" example sentences by connecting FEs to knowledge base (KB) concepts. For example, connecting Employer FE to company concept in the KB enables the understanding that any (unseen) company can also be FE examples. However, a naive adoption of existing KB conceptualization technique, focusing on 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 between instances and concepts. We thus propose a scalable k-truss clustering and a Markov Random Field (MRF) model leveraging interdependence between conceptinstance, concept-concept, and instance-instance pairs. Our extensive analysis with real-life data validates that our approach improves not only the quality of the identified concepts for FrameNet, but also that of applications such as selectional preference.
|Title of host publication||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Number of pages||7|
|Publication status||Published - 2016|
|Event||30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States|
Duration: 2016 Feb 12 → 2016 Feb 17
|Name||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Other||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Period||16/2/12 → 16/2/17|
Bibliographical noteFunding Information:
This research was supported by the MSIP (The Ministry of Science, ICT and Future Planning), Korea and Microsoft Research, under ICT/SW Creative research program supervised by the IITP (Institute for Information & Communications Technology Promotion) (IITP-2015-R2215-15-1009). S. Hwang is a corresponding author.
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence