Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes
Jiuding Yang, Jinwen Luo, Weidong Guo, Jerry Chen, Di Niu, Yu Xu

TL;DR
This paper introduces Mulco, a novel model for recognizing nested Chinese named entities, supported by a new dataset ChiNesE, and demonstrates its superior performance over existing methods.
Contribution
The paper presents Mulco, a new approach for Chinese nested entity recognition using multiple scopes, and releases ChiNesE, a dedicated dataset for this task.
Findings
Mulco outperforms baseline methods on ChiNesE.
Mulco achieves state-of-the-art results on ACE2005 Chinese corpus.
The dataset ChiNesE contains 20,000 sentences with 117,284 entities, 43.8% nested.
Abstract
Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition. NNER is where one entity may be part of a longer entity, and this may happen on multiple levels, as the term nested suggests. These nested structures make traditional sequence labeling methods unable to properly recognize all entities. While recent researches focus on designing better recognition methods for NNER in a variety of languages, the Chinese NNER (CNNER) still lacks attention, where a free-for-access, CNNER-specialized benchmark is absent. In this paper, we aim to solve CNNER problems by providing a Chinese dataset and a learning-based model to tackle the issue. To facilitate the research on this task, we release ChiNesE, a CNNER dataset with 20,000 sentences sampled from online passages of multiple domains, containing 117,284 entities…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Text and Document Classification Technologies
