Recognizing biomedical named entities based on the sentence vector/twin word embeddings conditioned bidirectional LSTM

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 10035 LNAI, Issue , 2016, Pages 165-176

  Li, Lishuang ^a   Jin, Liuke ^a   Jiang, Yuxin ^a   Huang, Degen ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

LSTM; Sentence vector; Twin word embeddings; Viterbi algorithm

Indexed keywords

BIG DATA; COMPLEX NETWORKS; COMPUTATIONAL LINGUISTICS; FEATURE EXTRACTION; LINGUISTICS; RECURRENT NEURAL NETWORKS; VITERBI ALGORITHM;

BIDIRECTIONAL RECURRENT NEURAL NETWORKS; BIOMEDICAL INFORMATION EXTRACTIONS; BIOMEDICAL NAMED ENTITY RECOGNITION; EMBEDDINGS; LINGUISTIC ANALYSIS; LONG-RANGE DEPENDENCIES; LSTM; NEURAL NETWORK SYSTEMS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84992494601     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-47674-2_15     Document Type: Conference Paper

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