Context Representation Context Self Attention Attentive Context Word Processor Fully Connected ELMo Word LSTM Entity Representation Hidden States Entity LSTM Hidden States Sentence LSTM Word Processor Char level Character Embedding Character LSTM Word level Word Emb Word Representation Postag Emb EL
This paper presents a novel framework, MGNER, for Multi Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be nonoverlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state of the art baselines up to 4.4% in terms of the F1 score among nested/non overlapping NER tasks.