Next Article in Journal
Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
Next Article in Special Issue
3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition
Previous Article in Journal
MAC Layer Protocols for Internet of Things: A Survey
Previous Article in Special Issue
Object Detection Network Based on Feature Fusion and Attention Mechanism
Article Menu
Issue 1 (January) cover image

Export Article

Version is current.

Open AccessArticle

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

School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(1), 17; https://doi.org/10.3390/fi11010017
Received: 31 October 2018 / Revised: 30 December 2018 / Accepted: 8 January 2019 / Published: 14 January 2019
(This article belongs to the Special Issue Innovative Topologies and Algorithms for Neural Networks)
  |  
PDF [2133 KB, uploaded 14 January 2019]
  |  

Abstract

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. View Full-Text
Keywords: LSTM-CRF model; elements recognition; linguistic features; POS syntactic rules LSTM-CRF model; elements recognition; linguistic features; POS syntactic rules
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Xu, D.; Ge, R.; Niu, Z. Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm. Future Internet 2019, 11, 17.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Future Internet EISSN 1999-5903 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top