@inproceedings{seminck-amsili-2017-computational,
title = "A Computational Model of Human Preferences for Pronoun Resolution",
author = "Seminck, Olga and
Amsili, Pascal",
editor = "Kunneman, Florian and
I{\~n}urrieta, Uxoa and
Camilleri, John J. and
Ardanuy, Mariona Coll",
booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-4006",
pages = "53--63",
abstract = "We present a cognitive computational model of pronoun resolution that reproduces the human interpretation preferences of the Subject Assignment Strategy and the Parallel Function Strategy. Our model relies on a probabilistic pronoun resolution system trained on corpus data. Factors influencing pronoun resolution are represented as features weighted by their relative importance. The importance the model gives to the preferences is in line with psycholinguistic studies. We demonstrate the cognitive plausibility of the model by running it on experimental items and simulating antecedent choice and reading times of human participants. Our model can be used as a new means to study pronoun resolution, because it captures the interaction of preferences.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="seminck-amsili-2017-computational">
<titleInfo>
<title>A Computational Model of Human Preferences for Pronoun Resolution</title>
</titleInfo>
<name type="personal">
<namePart type="given">Olga</namePart>
<namePart type="family">Seminck</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pascal</namePart>
<namePart type="family">Amsili</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics</title>
</titleInfo>
<name type="personal">
<namePart type="given">Florian</namePart>
<namePart type="family">Kunneman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Uxoa</namePart>
<namePart type="family">Iñurrieta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">John</namePart>
<namePart type="given">J</namePart>
<namePart type="family">Camilleri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mariona</namePart>
<namePart type="given">Coll</namePart>
<namePart type="family">Ardanuy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Valencia, Spain</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We present a cognitive computational model of pronoun resolution that reproduces the human interpretation preferences of the Subject Assignment Strategy and the Parallel Function Strategy. Our model relies on a probabilistic pronoun resolution system trained on corpus data. Factors influencing pronoun resolution are represented as features weighted by their relative importance. The importance the model gives to the preferences is in line with psycholinguistic studies. We demonstrate the cognitive plausibility of the model by running it on experimental items and simulating antecedent choice and reading times of human participants. Our model can be used as a new means to study pronoun resolution, because it captures the interaction of preferences.</abstract>
<identifier type="citekey">seminck-amsili-2017-computational</identifier>
<location>
<url>https://aclanthology.org/E17-4006</url>
</location>
<part>
<date>2017-04</date>
<extent unit="page">
<start>53</start>
<end>63</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A Computational Model of Human Preferences for Pronoun Resolution
%A Seminck, Olga
%A Amsili, Pascal
%Y Kunneman, Florian
%Y Iñurrieta, Uxoa
%Y Camilleri, John J.
%Y Ardanuy, Mariona Coll
%S Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F seminck-amsili-2017-computational
%X We present a cognitive computational model of pronoun resolution that reproduces the human interpretation preferences of the Subject Assignment Strategy and the Parallel Function Strategy. Our model relies on a probabilistic pronoun resolution system trained on corpus data. Factors influencing pronoun resolution are represented as features weighted by their relative importance. The importance the model gives to the preferences is in line with psycholinguistic studies. We demonstrate the cognitive plausibility of the model by running it on experimental items and simulating antecedent choice and reading times of human participants. Our model can be used as a new means to study pronoun resolution, because it captures the interaction of preferences.
%U https://aclanthology.org/E17-4006
%P 53-63
Markdown (Informal)
[A Computational Model of Human Preferences for Pronoun Resolution](https://aclanthology.org/E17-4006) (Seminck & Amsili, EACL 2017)
ACL