<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="king-cook-2021-now">
    <titleInfo>
        <title>Now, It’s Personal : The Need for Personalized Word Sense Disambiguation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Milton</namePart>
        <namePart type="family">King</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Paul</namePart>
        <namePart type="family">Cook</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Held Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors. This might not be captured with an author-agnostic word sense disambiguation (WSD) model that was trained on multiple authors. Our work finds that WordNet’s first senses, the predominant senses of our dataset’s genre, and the predominant senses of an author can all be different and therefore, author-agnostic models could perform well over the entire dataset, but poorly on individual authors. In this work, we explore methods for personalizing WSD models by tailoring existing state-of-the-art models toward an individual by exploiting the author’s sense distributions. We propose a novel WSD dataset and show that personalizing a WSD system with knowledge of an author’s sense distributions or predominant senses can greatly increase its performance.</abstract>
    <identifier type="citekey">king-cook-2021-now</identifier>
    <location>
        <url>https://aclanthology.org/2021.ranlp-1.79</url>
    </location>
    <part>
        <date>2021-09</date>
        <extent unit="page">
            <start>692</start>
            <end>700</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Now, It’s Personal : The Need for Personalized Word Sense Disambiguation
%A King, Milton
%A Cook, Paul
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F king-cook-2021-now
%X Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors. This might not be captured with an author-agnostic word sense disambiguation (WSD) model that was trained on multiple authors. Our work finds that WordNet’s first senses, the predominant senses of our dataset’s genre, and the predominant senses of an author can all be different and therefore, author-agnostic models could perform well over the entire dataset, but poorly on individual authors. In this work, we explore methods for personalizing WSD models by tailoring existing state-of-the-art models toward an individual by exploiting the author’s sense distributions. We propose a novel WSD dataset and show that personalizing a WSD system with knowledge of an author’s sense distributions or predominant senses can greatly increase its performance.
%U https://aclanthology.org/2021.ranlp-1.79
%P 692-700

Markdown (Informal)

[Now, It’s Personal : The Need for Personalized Word Sense Disambiguation](https://aclanthology.org/2021.ranlp-1.79) (King & Cook, RANLP 2021)

• Now, It’s Personal : The Need for Personalized Word Sense Disambiguation (King & Cook, RANLP 2021)

ACL

• Milton King and Paul Cook. 2021. Now, It’s Personal : The Need for Personalized Word Sense Disambiguation. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 692–700, Held Online. INCOMA Ltd..