In our lab meeting tomorrow, Vincent will introduce a review on Nested Named Entity Recognizion. A Zoom link will be posted to Twist on the morning of the meeting.
A review on Nested Named Entity Recognizion
Abstract: Named entity recognition (NER) is the task to extract some certain semantic entities, such as person, organization, etc, from a sentence or a paragraph. In other words, it detects the span and the semantic categories that each entity belongs to. NER plays an important role in many downstream tasks such as relation extraction, co-reference resolution and entity linking. Nested NER, namely, refers to the stuation where some entities may contain others. Due to the technical problems, not semantic ones, Nested NER had been ignored for a lone time. However, Nested NER is very common, especially in biomedical domain, and fine-grained entities provide a necessary and detailed information for the downstream tasks. This review mainly focuses on three parts: i. Sequence labeling model with multiple labels classification. ii. Sequence labeling model with modified Decoder. iii. Other models apart from sequence labeling.
Tuesday, May 19th, 09:30 a.m.
