Nested Entity Recognition Method Based On Multidimensional Features And Fuzzy Localization

Abstract Nested named entity recognition (NNER) aims to identify possibly overlapping named entities, which is a crucial and challenging sub-task in the field of named entity recognition. Traditional sequence labeling methods are unable to effectively deal with the NNER due to their linear structure; span-based methods require traversing and verifying every possible span, which leads to high complexity and data imbalance issues. To address these issues, this paper proposed a nested entity recognition method based on Multidimensional Features and Fuzzy Localization (MFFL). Firstly, this method adopted the shared encoding that fused three features of characters, words, and parts of speech to obtain a multidimensional feature vector representation of the text and obtained rich semantic information in the text. Secondly, we proposed to use the fuzzy localization to assist the model in pinpointing the potential locations of entities. Finally, in the entity classification, it used a window to expand the sub-sequence and enumerate possible candidate entities and predicted the classification labels of these candidate entities. In order to alleviate the problem of error propagation and effectively learn the correlation between fuzzy localization and classification labels, we adopted multi-task learning strategy. This paper conducted several experiments on two public datasets. The experimental results showed that the proposed method achieves ideal results in both nested entity recognition and non-nested entity recognition tasks, and significantly reduced the time complexity of nested entity recognition.