Abstract
This paper describes the TEDL system for the entity discovery and linking, which compete the CCKS2016 domain-specific entity discovery and linking task. Given one review text and one pre-constructed movie knowledge base (MKB) from the douban website, we need to firstly detect all the entity mentions, then link them to MKB’s entities. The traditional named entity detection (NED) and entity linking (EL) techniques cannot be applied to domain-specific knowledge base effectively, most of existing techniques just take extracted named entities as the input to the following EL task without considering the interdependency between the NED and EL and how to detect the Fake Named Entities (FNEs) [1]. In this paper, we employ one novel method described in [1] to joint model the 2 procedures as our basic system. Besides it, we also used the basic system’s output as features to train models. Finally we ensemble all the models’ output to predict FNE. The experiment results show that 80.30% NED F1 score and 93.45% EL accuracy, which is better than that of traditional methods.
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Yang, T., Zhang, F., Li, X., Jia, Q., Wang, C. (2016). Domain-Specific Entity Discovery and Linking Task. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_21
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DOI: https://doi.org/10.1007/978-981-10-3168-7_21
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