@article{roth-lapata-2015-context,
title = "Context-aware Frame-Semantic Role Labeling",
author = "Roth, Michael and
Lapata, Mirella",
editor = "Collins, Michael and
Lee, Lillian",
journal = "Transactions of the Association for Computational Linguistics",
volume = "3",
year = "2015",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q15-1032",
doi = "10.1162/tacl_a_00150",
pages = "449--460",
abstract = "Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="roth-lapata-2015-context">
<titleInfo>
<title>Context-aware Frame-Semantic Role Labeling</title>
</titleInfo>
<name type="personal">
<namePart type="given">Michael</namePart>
<namePart type="family">Roth</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mirella</namePart>
<namePart type="family">Lapata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2015</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<genre authority="bibutilsgt">journal article</genre>
<relatedItem type="host">
<titleInfo>
<title>Transactions of the Association for Computational Linguistics</title>
</titleInfo>
<originInfo>
<issuance>continuing</issuance>
<publisher>MIT Press</publisher>
<place>
<placeTerm type="text">Cambridge, MA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">periodical</genre>
<genre authority="bibutilsgt">academic journal</genre>
</relatedItem>
<abstract>Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.</abstract>
<identifier type="citekey">roth-lapata-2015-context</identifier>
<identifier type="doi">10.1162/tacl_a_00150</identifier>
<location>
<url>https://aclanthology.org/Q15-1032</url>
</location>
<part>
<date>2015</date>
<detail type="volume"><number>3</number></detail>
<extent unit="page">
<start>449</start>
<end>460</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Journal Article
%T Context-aware Frame-Semantic Role Labeling
%A Roth, Michael
%A Lapata, Mirella
%J Transactions of the Association for Computational Linguistics
%D 2015
%V 3
%I MIT Press
%C Cambridge, MA
%F roth-lapata-2015-context
%X Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.
%R 10.1162/tacl_a_00150
%U https://aclanthology.org/Q15-1032
%U https://doi.org/10.1162/tacl_a_00150
%P 449-460
Markdown (Informal)
[Context-aware Frame-Semantic Role Labeling](https://aclanthology.org/Q15-1032) (Roth & Lapata, TACL 2015)
ACL