Information 2019, 10(2), 46; https://doi.org/10.3390/info10020046
Attention-Based Joint Entity Linking with Entity Embedding
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
PDF [1399 KB, uploaded 1 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.Keywords:
entity linking; LSTM; CNN; CRF; Forward-Backward Algorithm
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).

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