Next Article in Journal
Fuzzy-Logic-Based, Obstacle Information-Aided Multiple-Model Target Tracking
Previous Article in Journal
CaACBIM: A Context-aware Access Control Model for BIM
Article Menu

Export Article

Open AccessArticle
Information 2019, 10(2), 46; https://doi.org/10.3390/info10020046

Attention-Based Joint Entity Linking with Entity Embedding

1,2,3
,
1,2,* , 1,2,3
and
2,4
1
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
4
Cloud Computing Center, Chinese Academy of Sciences, Dongguan 523808, China
*
Author to whom correspondence should be addressed.
Received: 4 December 2018 / Revised: 22 January 2019 / Accepted: 28 January 2019 / Published: 1 February 2019
Full-Text   |   PDF [1424 KB, uploaded 19 February 2019]   |  

Abstract

Entity linking (also called entity disambiguation) aims to map the mentions in a given document to their corresponding entities in a target knowledge base. In order to build a high-quality entity linking system, efforts are made in three parts: Encoding of the entity, encoding of the mention context, and modeling the coherence among mentions. For the encoding of entity, we use long short term memory (LSTM) and a convolutional neural network (CNN) to encode the entity context and entity description, respectively. Then, we design a function to combine all the different entity information aspects, in order to generate unified, dense entity embeddings. For the encoding of mention context, unlike standard attention mechanisms which can only capture important individual words, we introduce a novel, attention mechanism-based LSTM model, which can effectively capture the important text spans around a given mention with a conditional random field (CRF) layer. In addition, we take the coherence among mentions into consideration with a Forward-Backward Algorithm, which is less time-consuming than previous methods. Our experimental results show that our model obtains a competitive, or even better, performance than state-of-the-art models across different datasets. View Full-Text
Keywords: entity linking; LSTM; CNN; CRF; Forward-Backward Algorithm entity linking; LSTM; CNN; CRF; Forward-Backward Algorithm
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

Liu, C.; Li, F.; Sun, X.; Han, H. Attention-Based Joint Entity Linking with Entity Embedding. Information 2019, 10, 46.

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]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top