Deep Neural Architectures for Joint Named Entity Recognition and Disambiguation
By Qianwen Wang, Mizuho Iwaihara. 2019
72 citations recorded by the source corpus.
Current entity linking methods typically first apply a named entity recognition (NER) model to extract a named entity and classify it into a predefined category, then apply an entity disambiguation model to link the named entity to a corresponding entity in the reference knowledge base. However, these methods ignore the inter-relations between the two tasks. Our work jointly optimizes deep neural models of both NER and entity disambiguation. In the entity disambiguation task, our deep neural model includes a recursive neural network and convolutional neural network with attention mechanism. Our model compares similarities between a mention and candidate entities by simultaneously considering semantic and background information, including Wikipedia description pages, contexts where the mention occurs, and entity typing information. The experiments show that our model can effectively leverage the semantic information of context, and performs competitively to conventional approaches.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Erik Cambria
- Devamanyu Hazarika
- Soujanya Poria
- Tom Young
- Xiang Ren
- Navonil Majumder
- Percy Liang
- Louis-philippe Morency
Citations in the corpus
- Recent Trends in Deep Learning Based Natural Language Processing (2017, methodology): “77 [142] Semi-CRF jointly trained with linking 91.”
- Empower Sequence Labeling with Task-Aware Neural Language Model (2017, background): “There are several attempts (Luo et al. 2015; Yang, Salakhutdinov, and Cohen 2017) to enhance sequence labeling tasks by introducing additional annotations from other related tasks (e.”
- Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, methodology): “[140] SEMi-CRf JoiNTLY TRAiNED WiTH LiNkiNg 91.”