Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm

A state-of-the-art entity recognition system relies on deep learning under data-driven conditions. In this paper, we combine deep learning with linguistic features and propose the long short-term memory-conditional random field model (LSTM-CRF model) with the integrity algorithm. This approach is pr...

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Bibliographic Details
Published inFuture internet Vol. 11; no. 1; p. 17
Main Authors Xu, Dong, Ge, Ruping, Niu, Zhihua
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 14.01.2019
Subjects
Algorithms
Annotations
Annual reports
Artificial intelligence
Conditional random fields
Data mining
Deep learning
Dependence
Dictionaries
Integrity
International conferences
Internet
Investments
Language
Linguistics
Machine learning
Management Discussion & Analysis
Methods
Natural language
Natural language processing
Neural networks
Product reviews
Recognition
Sentiment analysis
Syntax
Trends
Canada
United States > US
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Summary:A state-of-the-art entity recognition system relies on deep learning under data-driven conditions. In this paper, we combine deep learning with linguistic features and propose the long short-term memory-conditional random field model (LSTM-CRF model) with the integrity algorithm. This approach is primarily based on the use of part-of-speech (POS) syntactic rules to correct the boundaries of LSTM-CRF model annotations and improve its performance by raising the integrity of the elements. The method incorporates the advantages of the data-driven method and dependency syntax, and improves the precision rate of the elements without losing recall rate. Experiments show that the integrity algorithm is not only easy to combine with the other neural network model, but the overall effect is better than several advanced methods. In addition, we conducted cross-domain experiments based on a multi-industry corpus in the financial field. The results indicate that the method can be applied to other industries.
ISSN:1999-5903
1999-5903
DOI:10.3390/fi11010017