@inproceedings{jansson-liu-2017-distributed,
title = "Distributed Representation, {LDA} Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media",
author = "Jansson, Patrick and
Liu, Shuhua",
editor = "Derczynski, Leon and
Xu, Wei and
Ritter, Alan and
Baldwin, Tim",
booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4420",
doi = "10.18653/v1/W17-4420",
pages = "154--159",
abstract = "This paper reports our participation in the W-NUT 2017 shared task on emerging and rare entity recognition from user generated noisy text such as tweets, online reviews and forum discussions. To accomplish this challenging task, we explore an approach that combines LDA topic modelling with deep learning on word level and character level embeddings. The LDA topic modelling generates topic representation for each tweet which is used as a feature for each word in the tweet. The deep learning component consists of two-layer bidirectional LSTM and a CRF output layer. Our submitted result performed at 39.98 (F1) on entity and 37.77 on surface forms. Our new experiments after submission reached a best performance of 41.81 on entity and 40.57 on surface forms.",
}
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%0 Conference Proceedings
%T Distributed Representation, LDA Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media
%A Jansson, Patrick
%A Liu, Shuhua
%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 jansson-liu-2017-distributed
%X This paper reports our participation in the W-NUT 2017 shared task on emerging and rare entity recognition from user generated noisy text such as tweets, online reviews and forum discussions. To accomplish this challenging task, we explore an approach that combines LDA topic modelling with deep learning on word level and character level embeddings. The LDA topic modelling generates topic representation for each tweet which is used as a feature for each word in the tweet. The deep learning component consists of two-layer bidirectional LSTM and a CRF output layer. Our submitted result performed at 39.98 (F1) on entity and 37.77 on surface forms. Our new experiments after submission reached a best performance of 41.81 on entity and 40.57 on surface forms.
%R 10.18653/v1/W17-4420
%U https://aclanthology.org/W17-4420
%U https://doi.org/10.18653/v1/W17-4420
%P 154-159
Markdown (Informal)
[Distributed Representation, LDA Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media](https://aclanthology.org/W17-4420) (Jansson & Liu, WNUT 2017)
ACL