DeepNNNER: Applying BLSTM CNNs and Extended Lexicons to Named Entity Recognition in Tweets
In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy User generated Text (WNUT) Shared Task #2: Named Entity Recognition in Twitter. Our shared task submission adopts the bidirectional LSTM CNN model of Chiu and Nichols (2016) as it has been shown to perform well on both newswire and Web texts. It uses word embeddings trained on large scale Web text collections together with text normalization to cope with the diversity in Web texts, and lexicons for target named entity classes constructed from publicly available sources. Extended evaluation comparing the effectiveness of various word embeddings, text normalization, and lexicon settings shows that our system achieves a maximum F1 score of 47.24, performance surpassing that of the shared task's second ranked system.
NUT@COLING