<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hosseini-pour-shamsfard-2019-eoann">
    <titleInfo>
        <title>EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Rayehe</namePart>
        <namePart type="family">Hosseini Pour</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mehrnoush</namePart>
        <namePart type="family">Shamsfard</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</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">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Researchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words or concepts are important parts of knowledge encoded in wordnets. As the use of wordnets becomes extensively widespread, extending the existing ones gets more attention. Manually construction and extension of lexico-semantic relations for WordNets or knowledge graphs are very time-consuming. Using automatic relation extraction methods can speed up this process. In this study, we exploit an ensemble of lstm and convolutional neural networks in a supervised manner to capture lexico-semantic relations which can either be used directly in NLP applications or compose the edges of wordnets. The whole procedure of learning vector space representation of relations is language independent. We used Princeton WordNet 3.1, FarsNet 3.0 (the Persian wordnet), Root09 and EVALution as golden standards to evaluate the predictive performance of our model and the results are comparable on the two languages. Empirical results demonstrate that our model outperforms the state of the art models.</abstract>
    <identifier type="citekey">hosseini-pour-shamsfard-2019-eoann</identifier>
    <identifier type="doi">10.26615/978-954-452-056-4_057</identifier>
    <location>
        <url>https://aclanthology.org/R19-1057</url>
    </location>
    <part>
        <date>2019-09</date>
        <extent unit="page">
            <start>481</start>
            <end>486</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks
%A Hosseini Pour, Rayehe
%A Shamsfard, Mehrnoush
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F hosseini-pour-shamsfard-2019-eoann
%X Researchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words or concepts are important parts of knowledge encoded in wordnets. As the use of wordnets becomes extensively widespread, extending the existing ones gets more attention. Manually construction and extension of lexico-semantic relations for WordNets or knowledge graphs are very time-consuming. Using automatic relation extraction methods can speed up this process. In this study, we exploit an ensemble of lstm and convolutional neural networks in a supervised manner to capture lexico-semantic relations which can either be used directly in NLP applications or compose the edges of wordnets. The whole procedure of learning vector space representation of relations is language independent. We used Princeton WordNet 3.1, FarsNet 3.0 (the Persian wordnet), Root09 and EVALution as golden standards to evaluate the predictive performance of our model and the results are comparable on the two languages. Empirical results demonstrate that our model outperforms the state of the art models.
%R 10.26615/978-954-452-056-4_057
%U https://aclanthology.org/R19-1057
%U https://doi.org/10.26615/978-954-452-056-4_057
%P 481-486

Markdown (Informal)

[EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks](https://aclanthology.org/R19-1057) (Hosseini Pour & Shamsfard, RANLP 2019)

• EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks (Hosseini Pour & Shamsfard, RANLP 2019)

ACL

• Rayehe Hosseini Pour and Mehrnoush Shamsfard. 2019. EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 481–486, Varna, Bulgaria. INCOMA Ltd..