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  • × author_ss:"Yu, Y."
  • × year_i:[2010 TO 2020}
  1. Luo, Z.; Yu, Y.; Osborne, M.; Wang, T.: Structuring tweets for improving Twitter search (2015) 0.00 
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    Abstract
    Spam and wildly varying documents make searching in Twitter challenging. Most Twitter search systems generally treat a Tweet as a plain text when modeling relevance. However, a series of conventions allows users to Tweet in structural ways using a combination of different blocks of texts. These blocks include plain texts, hashtags, links, mentions, etc. Each block encodes a variety of communicative intent and the sequence of these blocks captures changing discourse. Previous work shows that exploiting the structural information can improve the structured documents (e.g., web pages) retrieval. In this study we utilize the structure of Tweets, induced by these blocks, for Twitter retrieval and Twitter opinion retrieval. For Twitter retrieval, a set of features, derived from the blocks of text and their combinations, is used into a learning-to-rank scenario. We show that structuring Tweets can achieve state-of-the-art performance. Our approach does not rely on social media features, but when we do add this additional information, performance improves significantly. For Twitter opinion retrieval, we explore the question of whether structural information derived from the body of Tweets and opinionatedness ratings of Tweets can improve performance. Experimental results show that retrieval using a novel unsupervised opinionatedness feature based on structuring Tweets achieves comparable performance with a supervised method using manually tagged Tweets. Topic-related specific structured Tweet sets are shown to help with query-dependent opinion retrieval.
    Type
    a
  2. Cao, Y.; Duan, H.; Lin, C.-L.; Yu, Y.: Re-ranking question search results by clustering questions (2011) 0.00 
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    Abstract
    In this article, we address the problem of question clustering and study its use for re-ranking question search results. In question clustering we have to organize question search results into certain meaningful and condensed groups. Specifically, we propose to use a data structure consisting of question topic and question focus for modeling questions, and then cluster questions on the basis of the data structure. Experimental results show that our approach to question clustering improves the performance of question search significantly better than the approach not utilizing the topic-focus structure.
    Type
    a

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