Aggregated Semantic Matching for Short Text Entity Linking

Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 476–485 Brussels, Belgium, October 31 - November 1, 2018. c⃝2018 Association for Computational Linguistics 476 Aggregated Semantic Matching for Short Text Entity Linking Feng Nie1∗, Shuyan Zhou2∗, Jing Liu3∗, Jinpeng Wang4, Chin-Yew Lin4, Rong Pan1∗ 1Sun Yat-Sen University 2Harbin Institute of Technology 3Baidu Inc. 4Microsoft Research Asia {fengniesysu, alexisxy0418}@gmail.com, liujing46@baidu.com, {jinpwa, cyl}@microsoft.com, panr@sysu.edu.cn Abstract The task of entity linking aims to identify con- cepts mentioned in a text fragments and link them to a reference knowledge base. Entity linking in long text has been well studied in previous work. However, short text entity link- ing is more challenging since the texts are noisy and less coherent. To better utilize the local information provided in short texts, we propose a novel neural network framework, Aggregated Semantic Matching (ASM), in which two different aspects of semantic infor- mation between the local context and the can- didate entity are captured via representation- based and interaction-based neural semantic matching models, and then two matching sig- nals work jointly for disambiguation with a rank aggregation mechanism. Our evaluation shows that the proposed model outperforms the state-of-the-arts on public tweet datasets. 1 Introduction The task of entity linking aims to link a men- tion that appears in a piece of text to an entry (i.e. entity) in a knowledge base. For example, as shown in Table 1, given a mention Trump in a tweet, it should be linked to the entity Donald Trump1 in Wikipedia. Recent research has shown that entity linking can help better understand the text of a document (Schuhmacher and Ponzetto, 2014) and benefits several tasks, including named entity recognition (Luo et al.) and information re- trieval (Xiong et al., 2017b). The research of entity linking mainly considers two types of documents: long text (e.g. news articles and web documents) and short text (e.g. tweets). In this paper, we focus on short text, particularly tweet entity linking. ∗Correspondence author is Rong Pan. This work was done when the first and second author were interns and the third author was an employee at Microsoft Research Asia. 1https://en.wikipedia.org/wiki/Donald Trump Tweet The vile #Trump humanity raises its gentle face in Canada ... chapeau to #Trudeau Candidates Donald Trump, Trump (card games), ... Table 1: An illustration of short text entity linking, with mention Trump underlined. One of the major challenges in entity link- ing task is ambiguity, where an entity mention could denote to multiple entities in a knowledge base. As shown in Table 1, the mention Trump can refer to U.S. president Donald Trump and also the card name Trump (card games). Many of recent approaches for long text entity linking take the advantage of global context which cap- tures the coherence among the mapped entities for a set of related mentions in a single docu- ment (Cucerzan, 2007; Han et al., 2011; Glober- son et al., 2016; Heinzerling et al., 2017). How- ever, short texts like tweets are often concise and …

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