BOUNCE: An Efficient Selective Enumeration Approach for Nested Named Entity Recognition

Abstract

The scenario that one entity contains other entities is known as nested entities. Nested named entity recognition is a fundamental and challenging task in various NLP applications. The state-of-the-art nested NER approach first enumerates all the text spans in a sentence and then performs classification. We realize that a large proportion of entities contain only one token which cannot be nested, and most text spans in a sentence are not entities and the full enumeration is thus costly and unnecessary. In this paper, we propose an efficient selective enumeration approach named BOUNCE. We decompose the nested NER task into two subtasks for identifying unit-length entities and the others respectively. We develop a delicate model for each subtask and perform joint training for both of them. To improve the efficiency, we employ a head detection module to locate the start points of entities, which acts as a filtering step before enumeration. We provide a detailed analysis on the time complexity of the existing nested NER techniques and conduct extensive experiments on two datasets. The results demonstrate that BOUNCE outperforms various nested NER techniques and achieves higher efficiency than the state-of-the-art method with comparable accuracy performance.