Semantic Frame Labeling with Target-based Neural Model

Yukun Feng, Dong Yu, Jian Xu, Chunhua Liu


Abstract
This paper explores the automatic learning of distributed representations of the target’s context for semantic frame labeling with target-based neural model. We constrain the whole sentence as the model’s input without feature extraction from the sentence. This is different from many previous works in which local feature extraction of the targets is widely used. This constraint makes the task harder, especially with long sentences, but also makes our model easily applicable to a range of resources and other similar tasks. We evaluate our model on several resources and get the state-of-the-art result on subtask 2 of SemEval 2015 task 15. Finally, we extend the task to word-sense disambiguation task and we also achieve a strong result in comparison to state-of-the-art work.
Anthology ID:
S17-1010
Volume:
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
91–96
Language:
URL:
https://www.aclweb.org/anthology/S17-1010
DOI:
10.18653/v1/S17-1010
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-1010.pdf