<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sikdar-gamback-2017-feature">
    <titleInfo>
        <title>A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Utpal</namePart>
        <namePart type="given">Kumar</namePart>
        <namePart type="family">Sikdar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Björn</namePart>
        <namePart type="family">Gambäck</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 3rd Workshop on Noisy User-generated Text</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tim</namePart>
            <namePart type="family">Baldwin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Copenhagen, Denmark</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Detecting previously unseen named entities in text is a challenging task. The paper describes how three initial classifier models were built using Conditional Random Fields (CRFs), Support Vector Machines (SVMs) and a Long Short-Term Memory (LSTM) recurrent neural network. The outputs of these three classifiers were then used as features to train another CRF classifier working as an ensemble. 5-fold cross-validation based on training and development data for the emerging and rare named entity recognition shared task showed precision, recall and F1-score of 66.87%, 46.75% and 54.97%, respectively. For surface form evaluation, the CRF ensemble-based system achieved precision, recall and F1 scores of 65.18%, 45.20% and 53.30%. When applied to unseen test data, the model reached 47.92% precision, 31.97% recall and 38.55% F1-score for entity level evaluation, with the corresponding surface form evaluation values of 44.91%, 30.47% and 36.31%.</abstract>
    <identifier type="citekey">sikdar-gamback-2017-feature</identifier>
    <identifier type="doi">10.18653/v1/W17-4424</identifier>
    <location>
        <url>https://aclanthology.org/W17-4424</url>
    </location>
    <part>
        <date>2017-09</date>
        <extent unit="page">
            <start>177</start>
            <end>181</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities
%A Sikdar, Utpal Kumar
%A Gambäck, Björn
%Y Derczynski, Leon
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%S Proceedings of the 3rd Workshop on Noisy User-generated Text
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F sikdar-gamback-2017-feature
%X Detecting previously unseen named entities in text is a challenging task. The paper describes how three initial classifier models were built using Conditional Random Fields (CRFs), Support Vector Machines (SVMs) and a Long Short-Term Memory (LSTM) recurrent neural network. The outputs of these three classifiers were then used as features to train another CRF classifier working as an ensemble. 5-fold cross-validation based on training and development data for the emerging and rare named entity recognition shared task showed precision, recall and F1-score of 66.87%, 46.75% and 54.97%, respectively. For surface form evaluation, the CRF ensemble-based system achieved precision, recall and F1 scores of 65.18%, 45.20% and 53.30%. When applied to unseen test data, the model reached 47.92% precision, 31.97% recall and 38.55% F1-score for entity level evaluation, with the corresponding surface form evaluation values of 44.91%, 30.47% and 36.31%.
%R 10.18653/v1/W17-4424
%U https://aclanthology.org/W17-4424
%U https://doi.org/10.18653/v1/W17-4424
%P 177-181

Markdown (Informal)

[A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities](https://aclanthology.org/W17-4424) (Sikdar & Gambäck, WNUT 2017)

• A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities (Sikdar & Gambäck, WNUT 2017)

ACL

• Utpal Kumar Sikdar and Björn Gambäck. 2017. A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities. In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 177–181, Copenhagen, Denmark. Association for Computational Linguistics.