@inproceedings{feng-etal-2017-semantic,
title = "Semantic Frame Labeling with Target-based Neural Model",
author = "Feng, Yukun and
Yu, Dong and
Xu, Jian and
Liu, Chunhua",
booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-1010",
doi = "10.18653/v1/S17-1010",
pages = "91--96",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="feng-etal-2017-semantic">
<titleInfo>
<title>Semantic Frame Labeling with Target-based Neural Model</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yukun</namePart>
<namePart type="family">Feng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dong</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jian</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chunhua</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-aug</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vancouver, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">feng-etal-2017-semantic</identifier>
<identifier type="doi">10.18653/v1/S17-1010</identifier>
<location>
<url>https://aclanthology.org/S17-1010</url>
</location>
<part>
<date>2017-aug</date>
<extent unit="page">
<start>91</start>
<end>96</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Semantic Frame Labeling with Target-based Neural Model
%A Feng, Yukun
%A Yu, Dong
%A Xu, Jian
%A Liu, Chunhua
%S Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
%D 2017
%8 aug
%I Association for Computational Linguistics
%C Vancouver, Canada
%F feng-etal-2017-semantic
%X 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.
%R 10.18653/v1/S17-1010
%U https://aclanthology.org/S17-1010
%U https://doi.org/10.18653/v1/S17-1010
%P 91-96
Markdown (Informal)
[Semantic Frame Labeling with Target-based Neural Model](https://aclanthology.org/S17-1010) (Feng et al., SemEval 2017)
ACL
- Yukun Feng, Dong Yu, Jian Xu, and Chunhua Liu. 2017. Semantic Frame Labeling with Target-based Neural Model. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 91–96, Vancouver, Canada. Association for Computational Linguistics.