Relevance Feedback using Latent Information

Proceedings of the 5th International Joint Conference on Natural Language Processing, pages 1037–1045, Chiang Mai, Thailand, November 8 – 13, 2011. c⃝2011 AFNLP Relevance Feedback using Latent Information Jun Harashima Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan harashima@nlp.kuee.kyoto-u.ac.jp Sadao Kurohashi Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan kuro@i.kyoto-u.ac.jp Abstract We present a novel relevance feedback (RF) method that uses not only the sur- face information in texts, but also the la- tent information contained therein. In the proposed method, we infer the latent topic distribution in user feedback and in each document in the search results using latent Dirichlet allocation, and then we modify the search results so that documents with a similar topic distribution to that of the feedback are re-ranked higher. Evaluation results show that our method is effective for both explicit and pseudo RF, and that it has the advantage of performing well even when only a small amount of user feed- back is available. 1 Introduction The main purpose of information retrieval (IR) is to provide the user with documents that are rele- vant to his/her information needs. However, it is difficult to achieve this by one-off retrieval, since user queries are typically short and often ambigu- ous (Jansen et al., 2000). Relevance feedback (RF) is a technique to solve this problem. The basic procedure of RF is as fol- lows. First, a system obtains initial search results for a given query, and presents them to the user. The user then annotates some of the documents in the search results as being relevant or not, and the system modifies the search results using this feed- back. There are a variety of RF methods that depend on different retrieval models. Rocchio’s algorithm (Rocchio, 1971) and the Ide dec-hi method (Ide, 1971) are well-known RF methods for the vector space model (Salton et al., 1975). In the prob- abilistic model (Sp¨arck Jones et al., 2000), the weight of terms can be modified by feedback. For language modeling approaches (Ponte and Croft, 1998), Zhai and Lafferty (2001) proposed a fun- damental RF method. As described above, many methods have been proposed for RF. However, most of the previous methods use only the surface information in texts. That is, they ignore the latent information in texts, which could assist in improving IR performance. For example, they do not and cannot use the infor- mation of words for RF that do not appear in user feedback even if these words are highly probable from the latent topics of the feedback. In this paper, we explore a novel RF method for language modeling approaches. In the proposed method, we use not only the surface information in texts, but also the latent information contained therein. More specifically, we infer the latent topic distribution in user feedback and in each document in the search results using latent Dirichlet alloca- tion (LDA), and then we modify the search results so that documents with a similar topic distribution …

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