<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="steingrimsson-etal-2019-augmenting">
    <titleInfo>
        <title>Augmenting a BiLSTM Tagger with a Morphological Lexicon and a Lexical Category Identification Step</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Stein\thór</namePart>
        <namePart type="family">Steingrímsson</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Örvar</namePart>
        <namePart type="family">Kárason</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hrafn</namePart>
        <namePart type="family">Loftsson</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>Previous work on using BiLSTM models for PoS tagging has primarily focused on small tagsets. We evaluate BiLSTM models for tagging Icelandic, a morphologically rich language, using a relatively large tagset. Our baseline BiLSTM model achieves higher accuracy than any other previously published tagger, when not taking advantage of a morphological lexicon. When we extend the model by incorporating such data, we outperform the earlier state-of-the-art results by a significant margin. We also report on work in progress that attempts to address the problem of data sparsity inherent to morphologically detailed, fine-grained tagsets. We experiment with training a separate model on only the lexical category and using the coarse-grained output tag as an input into to the main model. This method further increases the accuracy and reduces the tagging errors by 21.3% compared to previous state-of-the-art results. Finally, we train and test our tagger on a new gold standard for Icelandic.</abstract>
    <identifier type="citekey">steingrimsson-etal-2019-augmenting</identifier>
    <identifier type="doi">10.26615/978-954-452-056-4_133</identifier>
    <location>
        <url>https://aclanthology.org/R19-1133</url>
    </location>
    <part>
        <date>2019-09</date>
        <extent unit="page">
            <start>1161</start>
            <end>1168</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Augmenting a BiLSTM Tagger with a Morphological Lexicon and a Lexical Category Identification Step
%A Steingrímsson, Stein\thór
%A Kárason, Örvar
%A Loftsson, Hrafn
%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 steingrimsson-etal-2019-augmenting
%X Previous work on using BiLSTM models for PoS tagging has primarily focused on small tagsets. We evaluate BiLSTM models for tagging Icelandic, a morphologically rich language, using a relatively large tagset. Our baseline BiLSTM model achieves higher accuracy than any other previously published tagger, when not taking advantage of a morphological lexicon. When we extend the model by incorporating such data, we outperform the earlier state-of-the-art results by a significant margin. We also report on work in progress that attempts to address the problem of data sparsity inherent to morphologically detailed, fine-grained tagsets. We experiment with training a separate model on only the lexical category and using the coarse-grained output tag as an input into to the main model. This method further increases the accuracy and reduces the tagging errors by 21.3% compared to previous state-of-the-art results. Finally, we train and test our tagger on a new gold standard for Icelandic.
%R 10.26615/978-954-452-056-4_133
%U https://aclanthology.org/R19-1133
%U https://doi.org/10.26615/978-954-452-056-4_133
%P 1161-1168

Markdown (Informal)

[Augmenting a BiLSTM Tagger with a Morphological Lexicon and a Lexical Category Identification Step](https://aclanthology.org/R19-1133) (Steingrímsson et al., RANLP 2019)

• Augmenting a BiLSTM Tagger with a Morphological Lexicon and a Lexical Category Identification Step (Steingrímsson et al., RANLP 2019)

ACL

• Steinþór Steingrímsson, Örvar Kárason, and Hrafn Loftsson. 2019. Augmenting a BiLSTM Tagger with a Morphological Lexicon and a Lexical Category Identification Step. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1161–1168, Varna, Bulgaria. INCOMA Ltd..