Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 836–845, Austin, Texas, November 1-5, 2016. c⃝2016 Association for Computational Linguistics Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter Qi Zhang, Yang Wang, Yeyun Gong, Xuanjing Huang Shanghai Key Laboratory of Data Science School of Computer Science, Fudan University Shanghai, P.R. China {qz, ywang14, yygong12, xjhuang}@fudan.edu.cn Abstract Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. The task of automatically extracting them have received considerable attention in recent decades. Different from previous studies, which are usually focused on automatically extracting keyphrases from documents or articles, in this study, we considered the problem of automatically extracting keyphrases from tweets. Because of the length limitations of Twitter-like sites, the performances of existing methods usually drop sharply. We proposed a novel deep recurrent neural network (RNN) model to combine keywords and context information to perform this problem. To evaluate the proposed method, we also constructed a large-scale dataset collected from Twitter. The experimental results showed that the proposed method performs significantly better than previous methods. 1 Introduction Keyphrases are usually the selected phrases that can capture the main topics described in a given docu- ment (Turney, 2000). They can provide users with highly condensed and valuable information, and there are a wide variety of sources for keyphrases, including web pages, research articles, books, and even movies. In contrast to keywords, keyphrases usually contain two or more words. Normally, the meaning representations of these phrases are more precise than those of single words. Moreover, along with the increasing development of the internet, this kind of summarization has received continuous consideration in recent years from both the academic and entiprise communities (Witten et al., 1999; Wan and Xiao, 2008; Jiang et al., 2009; Zhao et al., 2011; Tuarob et al., 2015). Because of the enormous usefulness of keyphrases, various studies have been conducted on the automatic extraction of keyphrases using different methods, including rich linguistic etfeatures (Barker and Cornacchia, 2000; Paukkeri al., 2008), supervised classification-based methods (Witten et al., 1999; Wu et al., 2005; Wang et al., 2006), ranking-based methods (Jiang et al., 2009), and clustering-based methods (Mori et al., 2007; Danilevsky et al., 2014). These methods usually focus on extracting keyphrases from a single document or multiple documents. Typically, a large number of words exist in even a document of moderate length, where a few hundred words or more is common. Hence, statistical and linguistic features can be considered to determine the importance of phrases. In addition to the previously mentioned methods, a few researchers have studied the problem of extracting keyphrases from collections of tweets (Zhao et al., 2011; Bellaachia and Al-Dhelaan, 2012). In contrast to traditional web applications, Twitter-like services usually limit the content length to 140 characters. In (Zhao et al., 2011), the context- sensitive topical PageRank method was proposed to extract keyphrases by topic from a collection of tweets. NE-Rank was also proposed to rank keywords for the …

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