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Learning for clinical named entity recognition without manual annotations

Informatics in Medicine Unlocked, 2018
Background: Named entity recognition (NER) systems are commonly built using supervised methods that use machine learning to learn from corpora manually annotated with named entities.
Omid Ghiasvand, Rohit J. Kate
doaj

SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT) [PDF]

, 2015
Wei Xu, Chris Callison-Burch, Bill Dolan
openalex +1 more source

SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing [PDF]

, 2015
Stephan Oepen +7 more
openalex +1 more source

SENTENCE REPRESENTATION USING LSTM FOR FINDING QUESTION

Tạp chí Khoa học
Learning sentence representation with the full semantics of a document is a challenge in natural language processing problems because if the semantic representation vector of the sentence is suitable, it will increase the performance of finding similar ...
Dinh Khanh Linh, Tran Quang Huy
doaj +1 more source

Aspect sentiment analysis based on a hierarchical attention network

大数据, 2020
Aspect sentiment analysis based on deep learning is one of the hot spots in natural language processing.Aiming at aspect sentiment,a deep hierarchical attention network model based on aspect sentiment analysis was proposed.The local features of the text ...
Ting SONG, Zhanwei CHEN, Haifeng YANG
doaj

UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering [PDF]