Exploiting Multiple Embeddings for Chinese Named Entity Recognition
Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level. However, due to the predominant usage of colloquial language in microblogs, the named entity recognition (NER) in Chinese microblogs experience significant performance deterioration, compared with performing NER in formal Chinese corpus. In this paper, we propose a simple yet effective neural framework to derive the character-level embeddings for NER in Chinese text, named ME-CNER. A character embedding is derived with rich semantic information harnessed at multiple granularities, ranging from radical, character to word levels. The experimental results demonstrate that the proposed approach achieves a large performance improvement on Weibo dataset and comparable performance on MSRA news dataset with lower computational cost against the existing state-of-the-art alternatives.
Code (1)
Tasks
Chinese Named Entity Recognitionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERSimilar Papers 제목 키워드 기반
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data
In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class. However, the number of non-relation entity pairs in context (negative instances…
Multi-Task Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+6Classification Attention for Chinese NER
The character-based model, such as BERT, has achieved remarkable success in Chinese named entity recognition (NER). However, such model would likely miss the overall information of the entity words. In this paper, we pro…
Chinese Named Entity RecognitionClassificationnamed-entity-recognitionNamed Entity Recognition+3Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings
Using Word Embeddings to Translate Named Entities
In this paper we investigate the usefulness of neural word embeddings in the process of translating Named Entities (NEs) from a resource-rich language to a language low on resources relevant to the task at hand, introduc…
Chinese Named Entity Recognitionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+4Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes
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 m…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Nested Named Entity Recognition