<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nguyen-etal-2021-uncertainty-aware">
    <titleInfo>
        <title>An Uncertainty-Aware Encoder for Aspect Detection</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Thi-Nhung</namePart>
        <namePart type="family">Nguyen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kiem-Hieu</namePart>
        <namePart type="family">Nguyen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Young-In</namePart>
        <namePart type="family">Song</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tuan-Dung</namePart>
        <namePart type="family">Cao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Scott</namePart>
            <namePart type="given">Wen-tau</namePart>
            <namePart type="family">Yih</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Punta Cana, Dominican Republic</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Aspect detection is a fundamental task in opinion mining. Previous works use seed words either as priors of topic models, as anchors to guide the learning of aspects, or as features of aspect classifiers. This paper presents a novel weakly-supervised method to exploit seed words for aspect detection based on an encoder architecture. The encoder maps segments and aspects into a low-dimensional embedding space. The goal is approximating similarity between segments and aspects in the embedding space and their ground-truth similarity generated from seed words. An objective function is proposed to capture the uncertainty of ground-truth similarity. Our method outperforms previous works on several benchmarks in various domains.</abstract>
    <identifier type="citekey">nguyen-etal-2021-uncertainty-aware</identifier>
    <identifier type="doi">10.18653/v1/2021.findings-emnlp.69</identifier>
    <location>
        <url>https://aclanthology.org/2021.findings-emnlp.69</url>
    </location>
    <part>
        <date>2021-11</date>
        <extent unit="page">
            <start>797</start>
            <end>806</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T An Uncertainty-Aware Encoder for Aspect Detection
%A Nguyen, Thi-Nhung
%A Nguyen, Kiem-Hieu
%A Song, Young-In
%A Cao, Tuan-Dung
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F nguyen-etal-2021-uncertainty-aware
%X Aspect detection is a fundamental task in opinion mining. Previous works use seed words either as priors of topic models, as anchors to guide the learning of aspects, or as features of aspect classifiers. This paper presents a novel weakly-supervised method to exploit seed words for aspect detection based on an encoder architecture. The encoder maps segments and aspects into a low-dimensional embedding space. The goal is approximating similarity between segments and aspects in the embedding space and their ground-truth similarity generated from seed words. An objective function is proposed to capture the uncertainty of ground-truth similarity. Our method outperforms previous works on several benchmarks in various domains.
%R 10.18653/v1/2021.findings-emnlp.69
%U https://aclanthology.org/2021.findings-emnlp.69
%U https://doi.org/10.18653/v1/2021.findings-emnlp.69
%P 797-806

Markdown (Informal)

[An Uncertainty-Aware Encoder for Aspect Detection](https://aclanthology.org/2021.findings-emnlp.69) (Nguyen et al., Findings 2021)

• An Uncertainty-Aware Encoder for Aspect Detection (Nguyen et al., Findings 2021)

ACL

• Thi-Nhung Nguyen, Kiem-Hieu Nguyen, Young-In Song, and Tuan-Dung Cao. 2021. An Uncertainty-Aware Encoder for Aspect Detection. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 797–806, Punta Cana, Dominican Republic. Association for Computational Linguistics.