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        <title>ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition.</title>
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    <name type="personal">
        <namePart type="given">Michel</namePart>
        <namePart type="given">Naim</namePart>
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        <namePart type="family">Salama</namePart>
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        <dateIssued>2016-dec</dateIssued>
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            <title>Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)</title>
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    <abstract>This paper describes the ASU system submitted in the COLING W-NUT 2016 Twitter Named Entity Recognition (NER) task. We present an experimental study on applying deep learning to extracting named entities (NEs) from tweets. We built two Long Short-Term Memory (LSTM) models for the task. The first model was built to extract named entities without types while the second model was built to extract and then classify them into 10 fine-grained entity classes. In this effort, we show detailed experimentation results on the effectiveness of word embeddings, brown clusters, part-of-speech (POS) tags, shape features, gazetteers, and local context for the tweet input vector representation to the LSTM model. Also, we present a set of experiments, to better design the network parameters for the Twitter NER task. Our system was ranked the fifth out of ten participants with a final f1-score for the typed classes of 39% and 55% for the non typed ones.</abstract>
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        <url>https://aclanthology.org/W16-3925</url>
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%0 Conference Proceedings
%T ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition.
%A Gerguis, Michel Naim
%A Salama, Cherif
%A El-Kharashi, M. Watheq
%S Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
%D 2016
%8 dec
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F gerguis-etal-2016-asu
%X This paper describes the ASU system submitted in the COLING W-NUT 2016 Twitter Named Entity Recognition (NER) task. We present an experimental study on applying deep learning to extracting named entities (NEs) from tweets. We built two Long Short-Term Memory (LSTM) models for the task. The first model was built to extract named entities without types while the second model was built to extract and then classify them into 10 fine-grained entity classes. In this effort, we show detailed experimentation results on the effectiveness of word embeddings, brown clusters, part-of-speech (POS) tags, shape features, gazetteers, and local context for the tweet input vector representation to the LSTM model. Also, we present a set of experiments, to better design the network parameters for the Twitter NER task. Our system was ranked the fifth out of ten participants with a final f1-score for the typed classes of 39% and 55% for the non typed ones.
%U https://aclanthology.org/W16-3925
%P 188-196

Markdown (Informal)

[ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition.](https://aclanthology.org/W16-3925) (Gerguis et al., 2016)

• ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition. (Gerguis et al., 2016)

ACL

• Michel Naim Gerguis, Cherif Salama, and M. Watheq El-Kharashi. 2016. ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition.. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 188–196, Osaka, Japan. The COLING 2016 Organizing Committee.