Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.
The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities.
(ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart.
Distributional models that learn rich semantic word representations are a success story of recent NLP research.
The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing.
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