<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rudrapal-das-2017-measuring">
    <titleInfo>
        <title>Measuring the Limit of Semantic Divergence for English Tweets.</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Dwijen</namePart>
        <namePart type="family">Rudrapal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Amitava</namePart>
        <namePart type="family">Das</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</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>In human language, an expression could be conveyed in many ways by different people. Even that the same person may express same sentence quite differently when addressing different audiences, using different modalities, or using different syntactic variations or may use different set of vocabulary. The possibility of such endless surface form of text while the meaning of the text remains almost same, poses many challenges for Natural Language Processing (NLP) systems like question-answering system, machine translation system and text summarization. This research paper is an endeavor to understand the characteristic of such endless semantic divergence. In this research work we develop a corpus of 1525 semantic divergent sentences for 200 English tweets.</abstract>
    <identifier type="citekey">rudrapal-das-2017-measuring</identifier>
    <identifier type="doi">10.26615/978-954-452-049-6_080</identifier>
    <part>
        <date>2017-09</date>
        <extent unit="page">
            <start>618</start>
            <end>624</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Measuring the Limit of Semantic Divergence for English Tweets.
%A Rudrapal, Dwijen
%A Das, Amitava
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F rudrapal-das-2017-measuring
%X In human language, an expression could be conveyed in many ways by different people. Even that the same person may express same sentence quite differently when addressing different audiences, using different modalities, or using different syntactic variations or may use different set of vocabulary. The possibility of such endless surface form of text while the meaning of the text remains almost same, poses many challenges for Natural Language Processing (NLP) systems like question-answering system, machine translation system and text summarization. This research paper is an endeavor to understand the characteristic of such endless semantic divergence. In this research work we develop a corpus of 1525 semantic divergent sentences for 200 English tweets.
%R 10.26615/978-954-452-049-6_080
%U https://doi.org/10.26615/978-954-452-049-6_080
%P 618-624

Markdown (Informal)

[Measuring the Limit of Semantic Divergence for English Tweets.](https://doi.org/10.26615/978-954-452-049-6_080) (Rudrapal & Das, RANLP 2017)

• Measuring the Limit of Semantic Divergence for English Tweets. (Rudrapal & Das, RANLP 2017)

ACL

• Dwijen Rudrapal and Amitava Das. 2017. Measuring the Limit of Semantic Divergence for English Tweets.. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 618–624, Varna, Bulgaria. INCOMA Ltd..