Abstract:

For the problem of insufficient feature extraction of named entity recognition for Chinese social media, a method of named entity recognition based on deep neural networks that combines a long short-term memory with a soft attention model is proposed in this article. A message from social media text is equivalent to a character sequence, so each character in the sequence should be converted into a corresponding character vector firstly. Secondly, a long short-term memory is used to extract the global text features from the converted character vector sequence. Thirdly, a soft attention model is used to extract the local text features from the global text feature vector sequence outputted by the previous step. Finally, a linear chain conditional random field is used to tag the named entities according to the global and local text feature vector sequence, and the results of named entity recognition are gotten and outputted. The results show that the proposed method in this article has a higher F-measure value compared with the baseline algorithm and the state-of-the-art algorithm.

Key words: named entity recognition, Chinese social media, deep neural network, attention mechanism