Attention-Based Joint Entity Linking with Entity Embedding
AbstractEntity 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
Share & Cite This Article
Liu, C.; Li, F.; Sun, X.; Han, H. Attention-Based Joint Entity Linking with Entity Embedding. Information 2019, 10, 46.
Liu C, Li F, Sun X, Han H. Attention-Based Joint Entity Linking with Entity Embedding. Information. 2019; 10(2):46.Chicago/Turabian Style
Liu, Chen; Li, Feng; Sun, Xian; Han, Hongzhe. 2019. "Attention-Based Joint Entity Linking with Entity Embedding." Information 10, no. 2: 46.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.