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        <title>Learning to Search for Recognizing Named Entities in Twitter</title>
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        <namePart type="given">Ioannis</namePart>
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        <namePart type="given">Cédric</namePart>
        <namePart type="family">Lopez</namePart>
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    <name type="personal">
        <namePart type="given">Nadia</namePart>
        <namePart type="family">Derbas</namePart>
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    <name type="personal">
        <namePart type="given">Ruslan</namePart>
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            <title>Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)</title>
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        <name type="personal">
            <namePart type="given">Bo</namePart>
            <namePart type="family">Han</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Wei</namePart>
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        <name type="personal">
            <namePart type="given">Tim</namePart>
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    <abstract>We presented in this work our participation in the 2nd Named Entity Recognition for Twitter shared task. The task has been cast as a sequence labeling one and we employed a learning to search approach in order to tackle it. We also leveraged LOD for extracting rich contextual features for the named-entities. Our submission achieved F-scores of 46.16 and 60.24 for the classification and the segmentation tasks and ranked 2nd and 3rd respectively. The post-analysis showed that LOD features improved substantially the performance of our system as they counter-balance the lack of context in tweets. The shared task gave us the opportunity to test the performance of NER systems in short and noisy textual data. The results of the participated systems shows that the task is far to be considered as a solved one and methods with stellar performance in normal texts need to be revised.</abstract>
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        <url>https://aclanthology.org/W16-3923</url>
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        <date>2016-12</date>
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            <start>171</start>
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%0 Conference Proceedings
%T Learning to Search for Recognizing Named Entities in Twitter
%A Partalas, Ioannis
%A Lopez, Cédric
%A Derbas, Nadia
%A Kalitvianski, Ruslan
%Y Han, Bo
%Y Ritter, Alan
%Y Derczynski, Leon
%Y Xu, Wei
%Y Baldwin, Tim
%S Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F partalas-etal-2016-learning
%X We presented in this work our participation in the 2nd Named Entity Recognition for Twitter shared task. The task has been cast as a sequence labeling one and we employed a learning to search approach in order to tackle it. We also leveraged LOD for extracting rich contextual features for the named-entities. Our submission achieved F-scores of 46.16 and 60.24 for the classification and the segmentation tasks and ranked 2nd and 3rd respectively. The post-analysis showed that LOD features improved substantially the performance of our system as they counter-balance the lack of context in tweets. The shared task gave us the opportunity to test the performance of NER systems in short and noisy textual data. The results of the participated systems shows that the task is far to be considered as a solved one and methods with stellar performance in normal texts need to be revised.
%U https://aclanthology.org/W16-3923
%P 171-177

Markdown (Informal)

[Learning to Search for Recognizing Named Entities in Twitter](https://aclanthology.org/W16-3923) (Partalas et al., WNUT 2016)

• Learning to Search for Recognizing Named Entities in Twitter (Partalas et al., WNUT 2016)

ACL

• Ioannis Partalas, Cédric Lopez, Nadia Derbas, and Ruslan Kalitvianski. 2016. Learning to Search for Recognizing Named Entities in Twitter. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 171–177, Osaka, Japan. The COLING 2016 Organizing Committee.