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    <titleInfo>
        <title>Projecting named entity recognizers without annotated or parallel corpora</title>
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        <namePart type="given">Jue</namePart>
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        <namePart type="given">Maximilian</namePart>
        <namePart type="family">Koppatz</namePart>
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    <name type="personal">
        <namePart type="given">José</namePart>
        <namePart type="given">María</namePart>
        <namePart type="given">Hoya</namePart>
        <namePart type="family">Quecedo</namePart>
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    <name type="personal">
        <namePart type="given">Roman</namePart>
        <namePart type="family">Yangarber</namePart>
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            <title>Proceedings of the 22nd Nordic Conference on Computational Linguistics</title>
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            <namePart type="given">Mareike</namePart>
            <namePart type="family">Hartmann</namePart>
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            <publisher>Linköping University Electronic Press</publisher>
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    <abstract>Named entity recognition (NER) is a well-researched task in the field of NLP, which typically requires large annotated corpora for training usable models. This is a problem for languages which lack large annotated corpora, such as Finnish. We propose an approach to create a named entity recognizer with no annotated or parallel documents, by leveraging strong NER models that exist for English. We automatically gather a large amount of chronologically matched data in two languages, then project named entity annotations from the English documents onto the Finnish ones, by resolving the matches with limited linguistic rules. We use this “artificially” annotated data to train a BiLSTM-CRF model. Our results show that this method can produce annotated instances with high precision, and the resulting model achieves state-of-the-art performance.</abstract>
    <identifier type="citekey">hou-etal-2019-projecting</identifier>
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        <url>https://aclanthology.org/W19-6124</url>
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        <date>2019-sep–oct</date>
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            <start>232</start>
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%0 Conference Proceedings
%T Projecting named entity recognizers without annotated or parallel corpora
%A Hou, Jue
%A Koppatz, Maximilian
%A Quecedo, José María Hoya
%A Yangarber, Roman
%Y Hartmann, Mareike
%Y Plank, Barbara
%S Proceedings of the 22nd Nordic Conference on Computational Linguistics
%D 2019
%8 sep–oct
%I Linköping University Electronic Press
%C Turku, Finland
%F hou-etal-2019-projecting
%X Named entity recognition (NER) is a well-researched task in the field of NLP, which typically requires large annotated corpora for training usable models. This is a problem for languages which lack large annotated corpora, such as Finnish. We propose an approach to create a named entity recognizer with no annotated or parallel documents, by leveraging strong NER models that exist for English. We automatically gather a large amount of chronologically matched data in two languages, then project named entity annotations from the English documents onto the Finnish ones, by resolving the matches with limited linguistic rules. We use this “artificially” annotated data to train a BiLSTM-CRF model. Our results show that this method can produce annotated instances with high precision, and the resulting model achieves state-of-the-art performance.
%U https://aclanthology.org/W19-6124
%P 232-241

Markdown (Informal)

[Projecting named entity recognizers without annotated or parallel corpora](https://aclanthology.org/W19-6124) (Hou et al., NoDaLiDa 2019)

• Projecting named entity recognizers without annotated or parallel corpora (Hou et al., NoDaLiDa 2019)

ACL

• Jue Hou, Maximilian Koppatz, José María Hoya Quecedo, and Roman Yangarber. 2019. Projecting named entity recognizers without annotated or parallel corpora. In Proceedings of the 22nd Nordic Conference on Computational Linguistics, pages 232–241, Turku, Finland. Linköping University Electronic Press.