Deep Affix Features Improve Neural Named Entity Recognizers

2018-06-01 · SEMEVAL 2018 6 · Vikas Yadav, Rebecca Sharp, Steven Bethard

We propose a practical model for named entity recognition (NER) that combines word and character-level information with a specific learned representation of the prefixes and suffixes of the word. We apply this approach to multilingual and multi-domain NER and show that it achieves state of the art results on the CoNLL 2002 Spanish and Dutch and CoNLL 2003 German NER datasets, consistently achieving 1.5-2.3 percent over the state of the art without relying on any dictionary features. Additionally, we show improvement on SemEval 2013 task 9.1 DrugNER, achieving state of the art results on the MedLine dataset and the second best results overall (-1.3{\%} from state of the art). We also establish a new benchmark on the I2B2 2010 Clinical NER dataset with 84.70 F-score.

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vikas95/Pref_Suff_Span_NN 공식 구현

Tasks

Feature EngineeringMorphological Analysisnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER

University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities

2019-06-01 · SEMEVAL 2019 6 · Vikas Yadav, Egoitz Laparra, Ti-Tai Wang, Mihai Surdeanu 외

We present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for the SemEval 2019 task 12. We achieved fourth place on tasks 1 and 3. We implemented a deep-affi…

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2019-08-23 · IJCNLP 2019 11 · Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig 외

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2019-09-01 · WS (NoDaLiDa) 2019 9 · Jue Hou, Maximilian Koppatz, José María Hoya Quecedo, Roman Yangarber

Named entity recognition (NER) is a well-researched task in the field of NLP, which typically requires large annotated corpora for training usable models. This is a problem for languages which lack large annotated corpor…