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Information 2019, 10(2), 46;

Attention-Based Joint Entity Linking with Entity Embedding

Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
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
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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

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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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