Leveraging multi-token entities in document-level named entity recognition

Proposed an attention-based document-level NER model that uses global context features (document-level) as well as local context features (sentence-level) for NER! Most NER model focuses on each sentence-level extraction independently and this could easily lead to confusion in entity types due to lack of a macro context. This paper focuses specifically on news articles and proposed MEID, Multi-token Entity Informed Document-level model for document-level NER. The whole idea being that we want to utilise the context information of multi-token entities as they are usually less ambiguous than single-token entities. In fact, 26.62% of single-token entities in CoNLL-2003 dataset are constituents of multi-token entities in the same document. The paper also designed an auxiliary task of Multi-token Entity Classification to detect multi-token entities and this is trained together with the document-level NER model without extra annotation!

Multi-grained named entity recognition

Proposed MGNER, Multi-Grained Named Entity Recognition, that is able to extract BOTH non-overlapping and nested entities. This is in contrast to previous work where models can either do one or the other! NER is common treated as a sequence labelling problem but this caused a major problem that it only trains models to recognise non-overlapping entities. There are now many approaches that can explicitly detect nested entities but they usually don’t perform well on non-overlapping entities. MGNER works by first detecting entity positions in different granularities using a Detector and then classify these entities into different categories using a Classifier. MGNER is highly modularised with 5 different modules:

  1. Word Processor

  2. Sentence Processor

  3. Entity Processor

  4. Detection Network

  5. Classification Network

This modularisation allows people to have high customisability in their NER models! MGNER achieved new SOTA results on both Nested and Non-overlapping NER tasks!



Data Scientist

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