<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="iter-etal-2020-entity">
    <titleInfo>
        <title>Entity Attribute Relation Extraction with Attribute-Aware Embeddings</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Dan</namePart>
        <namePart type="family">Iter</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiao</namePart>
        <namePart type="family">Yu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Fangtao</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Eneko</namePart>
            <namePart type="family">Agirre</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ivan</namePart>
            <namePart type="family">Vulić</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Entity-attribute relations are a fundamental component for building large-scale knowledge bases, which are widely employed in modern search engines. However, most such knowledge bases are manually curated, covering only a small fraction of all attributes, even for common entities. To improve the precision of model-based entity-attribute extraction, we propose attribute-aware embeddings, which embeds entities and attributes in the same space by the similarity of their attributes. Our model, EANET, learns these embeddings by representing entities as a weighted sum of their attributes and concatenates these embeddings to mention level features. EANET achieves up to 91% classification accuracy, outperforming strong baselines and achieves 83% precision on manually labeled high confidence extractions, outperforming Biperpedia (Gupta et al., 2014), a previous state-of-the-art for large scale entity-attribute extraction.</abstract>
    <identifier type="citekey">iter-etal-2020-entity</identifier>
    <identifier type="doi">10.18653/v1/2020.deelio-1.6</identifier>
    <location>
        <url>https://aclanthology.org/2020.deelio-1.6</url>
    </location>
    <part>
        <date>2020-11</date>
        <extent unit="page">
            <start>50</start>
            <end>55</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Entity Attribute Relation Extraction with Attribute-Aware Embeddings
%A Iter, Dan
%A Yu, Xiao
%A Li, Fangtao
%Y Agirre, Eneko
%Y Apidianaki, Marianna
%Y Vulić, Ivan
%S Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F iter-etal-2020-entity
%X Entity-attribute relations are a fundamental component for building large-scale knowledge bases, which are widely employed in modern search engines. However, most such knowledge bases are manually curated, covering only a small fraction of all attributes, even for common entities. To improve the precision of model-based entity-attribute extraction, we propose attribute-aware embeddings, which embeds entities and attributes in the same space by the similarity of their attributes. Our model, EANET, learns these embeddings by representing entities as a weighted sum of their attributes and concatenates these embeddings to mention level features. EANET achieves up to 91% classification accuracy, outperforming strong baselines and achieves 83% precision on manually labeled high confidence extractions, outperforming Biperpedia (Gupta et al., 2014), a previous state-of-the-art for large scale entity-attribute extraction.
%R 10.18653/v1/2020.deelio-1.6
%U https://aclanthology.org/2020.deelio-1.6
%U https://doi.org/10.18653/v1/2020.deelio-1.6
%P 50-55

Markdown (Informal)

[Entity Attribute Relation Extraction with Attribute-Aware Embeddings](https://aclanthology.org/2020.deelio-1.6) (Iter et al., DeeLIO 2020)

• Entity Attribute Relation Extraction with Attribute-Aware Embeddings (Iter et al., DeeLIO 2020)

ACL

• Dan Iter, Xiao Yu, and Fangtao Li. 2020. Entity Attribute Relation Extraction with Attribute-Aware Embeddings. In Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 50–55, Online. Association for Computational Linguistics.