<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lombardo-etal-2020-top">
    <titleInfo>
        <title>Top-Rank-Focused Adaptive Vote Collection for the Evaluation of Domain-Specific Semantic Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Pierangelo</namePart>
        <namePart type="family">Lombardo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alessio</namePart>
        <namePart type="family">Boiardi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Luca</namePart>
        <namePart type="family">Colombo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Angelo</namePart>
        <namePart type="family">Schiavone</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nicolò</namePart>
        <namePart type="family">Tamagnone</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-nov</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The growth of domain-specific applications of semantic models, boosted by the recent achievements of unsupervised embedding learning algorithms, demands domain-specific evaluation datasets. In many cases, content-based recommenders being a prime example, these models are required to rank words or texts according to their semantic relatedness to a given concept, with particular focus on top ranks. In this work, we give a threefold contribution to address these requirements: (i) we define a protocol for the construction, based on adaptive pairwise comparisons, of a relatedness-based evaluation dataset tailored on the available resources and optimized to be particularly accurate in top-rank evaluation; (ii) we define appropriate metrics, extensions of well-known ranking correlation coefficients, to evaluate a semantic model via the aforementioned dataset by taking into account the greater significance of top ranks. Finally, (iii) we define a stochastic transitivity model to simulate semantic-driven pairwise comparisons, which confirms the effectiveness of the proposed dataset construction protocol.</abstract>
    <identifier type="citekey">lombardo-etal-2020-top</identifier>
    <identifier type="doi">10.18653/v1/2020.emnlp-main.249</identifier>
    <location>
        <url>https://aclanthology.org/2020.emnlp-main.249</url>
    </location>
    <part>
        <date>2020-nov</date>
        <extent unit="page">
            <start>3081</start>
            <end>3093</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Top-Rank-Focused Adaptive Vote Collection for the Evaluation of Domain-Specific Semantic Models
%A Lombardo, Pierangelo
%A Boiardi, Alessio
%A Colombo, Luca
%A Schiavone, Angelo
%A Tamagnone, Nicolò
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F lombardo-etal-2020-top
%X The growth of domain-specific applications of semantic models, boosted by the recent achievements of unsupervised embedding learning algorithms, demands domain-specific evaluation datasets. In many cases, content-based recommenders being a prime example, these models are required to rank words or texts according to their semantic relatedness to a given concept, with particular focus on top ranks. In this work, we give a threefold contribution to address these requirements: (i) we define a protocol for the construction, based on adaptive pairwise comparisons, of a relatedness-based evaluation dataset tailored on the available resources and optimized to be particularly accurate in top-rank evaluation; (ii) we define appropriate metrics, extensions of well-known ranking correlation coefficients, to evaluate a semantic model via the aforementioned dataset by taking into account the greater significance of top ranks. Finally, (iii) we define a stochastic transitivity model to simulate semantic-driven pairwise comparisons, which confirms the effectiveness of the proposed dataset construction protocol.
%R 10.18653/v1/2020.emnlp-main.249
%U https://aclanthology.org/2020.emnlp-main.249
%U https://doi.org/10.18653/v1/2020.emnlp-main.249
%P 3081-3093

Markdown (Informal)

[Top-Rank-Focused Adaptive Vote Collection for the Evaluation of Domain-Specific Semantic Models](https://aclanthology.org/2020.emnlp-main.249) (Lombardo et al., EMNLP 2020)

• Top-Rank-Focused Adaptive Vote Collection for the Evaluation of Domain-Specific Semantic Models (Lombardo et al., EMNLP 2020)

ACL

• Pierangelo Lombardo, Alessio Boiardi, Luca Colombo, Angelo Schiavone, and Nicolò Tamagnone. 2020. Top-Rank-Focused Adaptive Vote Collection for the Evaluation of Domain-Specific Semantic Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3081–3093, Online. Association for Computational Linguistics.