Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts

Proceedings of NAACL-HLT 2018, pages 1676–1686 New Orleans, Louisiana, June 1 - 6, 2018. c⃝2018 Association for Computational Linguistics Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts Yingyi Zhang†∗ Jing Li‡ Yan Song‡ Chengzhi Zhang† †Nanjing University of Science and Technology {yingyizhang, zhangcz}@njust.edu.cn ‡Tencent AI Lab {ameliajli,clksong}@tencent.com Abstract Existing keyphrase extraction methods suffer from data sparsity problem when they are con- ducted on short and informal texts, especially microblog messages. Enriching context is one way to alleviate this problem. Considering that conversations are formed by reposting and re- plying messages, they provide useful clues for recognizing essential content in target posts and are therefore helpful for keyphrase iden- tification. In this paper, we present a neural keyphrase extraction framework for microblog posts that takes their conversation context into account, where four types of neural encoders, namely, averaged embedding, RNN, attention, and memory networks, are proposed to repre- sent the conversation context. Experimental results on Twitter and Weibo datasets1 show that our framework with such encoders outper- forms state-of-the-art approaches. 1 Introduction The increasing popularity of microblogs results in a huge volume of daily-produced user-generated data. As a result, such explosive growth of data far outpaces human beings’ reading and understand- ing capacity. Techniques that can automatically identify critical excerpts from microblog posts are therefore in growing demand. Keyphrase extrac- tion is one of the techniques that can meet this demand, because it is defined to identify salient phrases, generally formed by one or multiple words, for representing key focus and main topics for a given collection (Turney, 2000; Zhao et al., 2011). Particularly for microblogs, keyphrase ex- traction has been proven useful to downstream applications such as information retrieval (Choi *Work was done during the internship at Tencent AI Lab. 1Our datasets are released at: http://ai.tencent. com/ailab/Encoding_Conversation_Context_ for_Neural_Keyphrase_Extraction_from_ Microblog_Posts.html Target post for keyphrase extraction: ”I will curse you in that forum” is the lowest of low. You are an embarrassment president Duterte. Childish! Messages forming a conversation: [R1]: any head of state will be irked if asked to report to another head of state [R2]: Really? Did Obama really asked Duterte to report to him? LOL Table 1: An example conversation about “presi- dent Duterte” on Twitter. [Ri]: The i-th message in conversation ordered by their positing time. pres- ident Duterte: keyphrase to be detected; Italic words: words that are related to the main topic in conversations and can indicate the keyphrase. et al., 2012), text summarization (Zhao et al., 2011), event tracking (Ribeiro et al., 2017), etc. To date, most efforts on keyphrase extraction on microblogs treat messages as independent docu- ments or sentences, and then apply ranking-based models (Zhao et al., 2011; Bellaachia and Al- Dhelaan, 2012; Marujo et al., 2015) or sequence tagging models (Zhang et al., 2016) on them. It is arguable that these methods are suboptimal for recognizing salient content from short and infor- mal messages due to the severe data sparsity prob- lem. Considering that microblogs allow users to form conversations on …

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