Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach

摘要

The Secretary of Homeland Security was in attendance. Named Entity Recognition (NER) aims to extract and classify entity mentions in the text into pre-defined types (e.g., organization or person name). Recently, many works have been proposed to shape the NER as a machine reading comprehension problem (also termed MRC-based NER), in which entity recognition is achieved by answering the formulated questions related to pre-defined entity types through MRC, based on the contexts. However, these works ignore the label dependencies among entity types, which are critical for precisely recognizing named entities. In this paper, we propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER. We decompose MRC-based NER into multiple tasks and use a self-attention module to capture label dependencies. Comprehensive experiments on both nested NER and flat NER datasets are conducted to validate the effectiveness of the proposed Multi-NER. Experimental results show that Multi-NER can achieve better performance on all datasets. ORG PER Figure 1: An example of nested NER. and Manning, 2009). Figure 1 shows an example of nested NER: Homeland Security can be recognized as O RGANIZATION , as well as P ERSON. To mitigate this issue, many works resort to formulating NER as a Machine Reading Comprehension (MRC) question answering task (termed MRC-based NER) (Wang et al., 2020; Li et al., 2020; Wang et al., 2022). For example, to recognize the O RGANIZATION, a natural-language question “Which O RGANIZATION is mentioned in the text?” is formulated. Then the goal of NER is transformed to answer the formulated questions through machine reading comprehension, given the contexts. MRC-based NER provides a unified solution for both flat and nested NER tasks since each entity type has its corresponding entity span positions as the answer, and these output answers are independent of each other. Despite much progress having been made in MRC-based NER, existing approaches tend to ignore the label dependencies among entity types, which are critical for precise NER. Label dependencies indicate that different entities in the text have some relations with each other. For example, in a sentence “Ousted WeWork founder Adam Neumann lists his Manhattan penthouse for $37.5 million”, once Adam Neumann is recognized as P ERSON, it is expected to help with the recognition