@inproceedings{kurtz-etal-2019-improving,
title = "Improving Semantic Dependency Parsing with Syntactic Features",
author = "Kurtz, Robin and
Roxbo, Daniel and
Kuhlmann, Marco",
editor = {Nivre, Joakim and
Derczynski, Leon and
Ginter, Filip and
Lindi, Bj{\o}rn and
Oepen, Stephan and
S{\o}gaard, Anders and
Tidemann, J{\"o}rg},
booktitle = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
month = sep,
year = "2019",
address = "Turku, Finland",
publisher = {Link{\"o}ping University Electronic Press},
url = "https://aclanthology.org/W19-6202",
pages = "12--21",
abstract = "We extend a state-of-the-art deep neural architecture for semantic dependency parsing with features defined over syntactic dependency trees. Our empirical results show that only gold-standard syntactic information leads to consistent improvements in semantic parsing accuracy, and that the magnitude of these improvements varies with the specific combination of the syntactic and the semantic representation used. In contrast, automatically predicted syntax does not seem to help semantic parsing. Our error analysis suggests that there is a significant overlap between syntactic and semantic representations.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kurtz-etal-2019-improving">
<titleInfo>
<title>Improving Semantic Dependency Parsing with Syntactic Features</title>
</titleInfo>
<name type="personal">
<namePart type="given">Robin</namePart>
<namePart type="family">Kurtz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Roxbo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marco</namePart>
<namePart type="family">Kuhlmann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2019-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Joakim</namePart>
<namePart type="family">Nivre</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Leon</namePart>
<namePart type="family">Derczynski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Filip</namePart>
<namePart type="family">Ginter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bjørn</namePart>
<namePart type="family">Lindi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Stephan</namePart>
<namePart type="family">Oepen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anders</namePart>
<namePart type="family">Søgaard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jörg</namePart>
<namePart type="family">Tidemann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Linköping University Electronic Press</publisher>
<place>
<placeTerm type="text">Turku, Finland</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We extend a state-of-the-art deep neural architecture for semantic dependency parsing with features defined over syntactic dependency trees. Our empirical results show that only gold-standard syntactic information leads to consistent improvements in semantic parsing accuracy, and that the magnitude of these improvements varies with the specific combination of the syntactic and the semantic representation used. In contrast, automatically predicted syntax does not seem to help semantic parsing. Our error analysis suggests that there is a significant overlap between syntactic and semantic representations.</abstract>
<identifier type="citekey">kurtz-etal-2019-improving</identifier>
<location>
<url>https://aclanthology.org/W19-6202</url>
</location>
<part>
<date>2019-09</date>
<extent unit="page">
<start>12</start>
<end>21</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Improving Semantic Dependency Parsing with Syntactic Features
%A Kurtz, Robin
%A Roxbo, Daniel
%A Kuhlmann, Marco
%Y Nivre, Joakim
%Y Derczynski, Leon
%Y Ginter, Filip
%Y Lindi, Bjørn
%Y Oepen, Stephan
%Y Søgaard, Anders
%Y Tidemann, Jörg
%S Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing
%D 2019
%8 September
%I Linköping University Electronic Press
%C Turku, Finland
%F kurtz-etal-2019-improving
%X We extend a state-of-the-art deep neural architecture for semantic dependency parsing with features defined over syntactic dependency trees. Our empirical results show that only gold-standard syntactic information leads to consistent improvements in semantic parsing accuracy, and that the magnitude of these improvements varies with the specific combination of the syntactic and the semantic representation used. In contrast, automatically predicted syntax does not seem to help semantic parsing. Our error analysis suggests that there is a significant overlap between syntactic and semantic representations.
%U https://aclanthology.org/W19-6202
%P 12-21
Markdown (Informal)
[Improving Semantic Dependency Parsing with Syntactic Features](https://aclanthology.org/W19-6202) (Kurtz et al., NoDaLiDa 2019)
ACL