Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts
Existing keyphrase extraction methods suffer from data sparsity problem when they are conducted 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 replying messages, they provide useful clues for recognizing essential content in target posts and are therefore helpful for keyphrase identification. 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 represent the conversation context. Experimental results on Twitter and Weibo datasets show that our framework with such encoders outperforms state-of-the-art approaches.
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalKeyphrase ExtractionText SummarizationSimilar Papers 제목 키워드 기반
Enhancing Keyphrase Extraction from Microblogs using Human Reading Time
The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human readin…
Keyphrase ExtractionUsing Human Attention to Extract Keyphrase from Microblog Post
This paper studies automatic keyphrase extraction on social media. Previous works have achieved promising results on it, but they neglect human reading behavior during keyphrase annotating. The human attention is a cruci…
Keyphrase ExtractionUtilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs
Microblogging platforms generate massive amounts of short, noisy, and dispersed user content, making automatic keyphrase extraction (AKE) an important but challenging task. Prior studies have used eye-tracking signals to…
Keyphrase ExtractionA Joint Model of Conversational Discourse and Latent Topics on Microblogs
Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurre…
Topic ModelsA Joint Model of Conversational Discourse Latent Topics on Microblogs
Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurre…
Topic Models