About a year ago I wrote a blog post about recent research in Deep Learning for Natural Language Processing covering several subareas. One of the areas I didn’t cover was Deep Learning for Named Entity Recognition – so here are some interesting recent (2015-2016) papers related to that:
- Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks – authors: M Francis
- Entity Attribute Extraction from Unstructured Text with Deep Belief Network – authors: B Zhong, L Kong, J Liu
- Learning Word Segmentation Representations to Improve Named Entity Recognition for Chinese Social Media – authors: N Peng, M Dredze
- Biomedical Named Entity Recognition based on Deep Neutral Network – authors: L Yao, H Liu, Y Liu, X Li, MW Anwar
- Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexical normalization and named entity recognition – authors: T Baldwin, MC de Marneffe, B Han, YB Kim, A Ritter…
- Semi-Supervised Approach to Named Entity Recognition in Spanish Applied to a Real-World Conversational System – authors: SS Bojórquez, VM González
- Boosting Named Entity Recognition with Neural Character Embeddings – authors: C dos Santos, V Guimaraes, RJ Niterói, R de Janeiro
- Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text – authors: L Li, L Jin, D Huang
- Entity-centric search: querying by entities and for entities – authors: M Zhou
- Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach – authors: X Ren, A El
- Boosting Named Entity Recognition with Neural Character Embeddings – authors: CN Santos, V Guimarães
- Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network. – authors: Y Wu, M Jiang, J Lei, H Xu
- Context-aware Entity Morph Decoding – authors: B Zhang, H Huang, X Pan, S Li, CY Lin, H Ji, K Knight…
- Training word embeddings for deep learning in biomedical text mining tasks – authors: Z Jiang, L Li, D Huang, L Jin
- Entity Attribute Extraction from Unstructured Text with Deep Belief Network – authors: B Zhong, L Kong, J Liu
- Building Text-mining Framework for Gene-Phenotype Relation Extraction using Deep Leaning – authors: D Jang, J Lee, K Kim, D Lee
- Text Mining in Social Media for Security Threats – authors: D Inkpen
- Text Understanding from Scratch – authors: X Zhang, Y LeCun
- Syntax-based Deep Matching of Short Texts – authors: M Wang, Z Lu, H Li, Q Liu
- PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks – authors: J Tang, M Qu, Q Mei
- Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach – authors: X Ren, A El
- Domain-Specific Semantic Relatedness from Wikipedia Structure: A Case Study in Biomedical Text – authors: A Sajadi, EE Milios, V Kešelj, JCM Janssen
- Deep Unordered Composition Rivals Syntactic Methods for Text Classification – authors: M Iyyer, V Manjunatha, J Boyd
- Representing Text for Joint Embedding of Text and Knowledge Bases – authors: K Toutanova, D Chen, P Pantel, H Poon, P Choudhury…
- In Defense of Word Embedding for Generic Text Representation – authors: G Lev, B Klein, L Wolf
Best regards,
Amund Tveit ()
btw: if you want to work (with me) as a Data Scientist on Deep Learning, check out this position