Abstract:

The semantics of Chinese vocabulary have a certain degree of ambiguity. In Chinese text， there are some features that have low relevance to named entity recognition. The same vocabulary has different meanings in different contexts， and different vocabulary and phrases have different contributions to named entity recognition. If weighting or masking operations are not performed， these features will interfere with the recognition accuracy of the model. To this end， a Chinese named entity recognition （CNER） algorithm with soft attention mask embedding is studied. Establish a multi-level CNER model， in the word vector representation layer of the model， use jieba technology to perform segmentation processing on the Chinese text passed from the input layer， and use Word2Vec method to obtain the word vectors of each vocabulary， forming a sequence of word vectors. In the BiLSTM layer， bidirectional long short-term memory processing is applied to the sequence of word vectors to obtain feature vectors that fuse contextual information for each word vector. Embedding a soft attention mask module after the BiLSTM layer， using the soft attention mechanism of this module to perform weighted and masked operations on the feature vectors output by the BiLSTM layer， focusing on features that contribute significantly to entity recognition， removing and suppressing unimportant features， and improving recognition accuracy. Label and decode the feature vectors processed by the soft attention mask module in the CRF layer to obtain the optimal entity label sequence， which is the Chinese named entity recognition result. The experiment shows that the algorithm can accurately recognize Chinese named entities， and has good performance in entity label annotation coverage and F1 value.

Key words: Chinese naming, soft attention, entity recognition, mask operation, Word2Vec, BiLSTM model