Passage-based retrieval models have been studied for some time and have been shown to have some benefits for document ranking. Finding passages that are not only topically relevant, but are also answers to the users' questions would have a significant impact in applications such as mobile search. To develop models for answer passage retrieval, we need to have appropriate test collections and evaluation measures. Making annotations at the passage level is, however, expensive and can have poor coverage. In this paper, we describe the advantages of document summarization measures for evaluating answer passage retrieval and show that these measures have high correlation with existing measures and human judgments.

The goal of a blog retrieval system is to retrieve and rank blogs, as collections of documents, in response to a given query. Previous studies have shown that diversity among the top retrieved posts from a blog is a positive feature for indicating relevance of the blog to the query. However, existing methods capture the diversity of a blog using post-level properties that limits their application to a specific category of retrieval methods. In this paper, we propose a blog-level diversity measure where there is no assumption made about the underlying blog-ranking technique. The proposed measure enables us to integrate diversity in any existing blog retrieval method. Our experimental results show that the proposed method, while being more general, produces comparable results to the post-level diversity detection methods.

Blog distillation (blog feed retrieval) is a task in blog retrieval where the goal is to rank blogs according to their recurrent relevance to a query topic. One of the main properties of blog feed retrieval is that the unit of retrieval is a collection of documents as opposed to a single document as in other IR tasks. This collection retrieval nature of blog distillation introduces new challenges and requires new investigations specific to this problem.

Patent prior art queries are full patent applications which are much longer than standard web search topics. Such queries are composed of hundreds of terms and do not represent a focused information need. One way to make the queries more focused is to select a group of key terms as representatives. Existing works show that such a selection to reduce patent queries is a challenging task mainly because of the presence of ambiguous terms. Given this setup, we present a query modeling approach where we utilize patent-specific characteristics to generate more precise queries. We propose to automatically disambiguate query terms by employing noun phrases that are extracted using the global analysis of the patent collection. We further introduce a method for predicting whether expansion using noun phrases would improve the retrieval effectiveness.

Abstract The goal in blog search is to rank blogs according to their recurrent relevance to the topic of the query. State‐of‐the‐art approaches view it as an expert search or resource selection problem. We investigate the effect of content‐based similarity between posts on the performance of the retrieval system. We test two different approaches for smoothing (regularizing) relevance scores of posts based on their dependencies. In the first approach, we smooth term distributions describing posts by performing a random walk over a document‐term graph in which similar posts are highly connected. In the second, we directly smooth scores for posts using a regularization framework that aims to minimize the discrepancy between scores for similar documents. We then extend these approaches to consider the time interval between the posts in smoothing the scores. The idea is that if two posts are temporally close, then they are good sources for smoothing each other's relevance scores. We compare these methods with the state‐of‐the‐art approaches in blog search that employ Language Modeling‐based resource selection algorithms and fusion‐based methods for aggregating post relevance scores. We show performance gains over the baseline techniques which do not take advantage of the relation between posts for smoothing relevance estimates.

Pseudo relevance feedback (PRF) is one of effective practices in Information Retrieval. In particular, PRF via the relevance model (RM) has been widely used due to the theoretical soundness and effectiveness. In a PRF scenario, an underlying relevance model is inferred by combining language models of the top retrieved documents where the contribution of each document is assumed to be proportional to its score for the initial query. However, it is not clear that selecting the top retrieved documents only by the initial retrieval scores is actually the optimal way for query expansion.

Blog post opinion retrieval is the problem of ranking blog posts according to the likelihood that the post is relevant to the query and that the author was expressing an opinion about the topic (of the query). A recent study has proposed a method for finding the opinion density at query term positions in a document which uses the proximity of query term and opinion term as an indicator of their relatedness. The maximum opinion density between different query positions was used as an opinion score of the whole document. In this paper we investigate the effect of exploiting multiple opinion evidence of a document. We propose using the ordered weighted averaging (OWA) operator in order to combine the opinion score of different query positions for a final score of a document, in the proximity-based opinion retrieval system.

This paper addresses blog feed retrieval where the goal is to retrieve the most relevant blog feeds for a given user query. Since the retrieval unit is a blog, as a collection of posts, performing relevance feedback techniques and selecting the most appropriate documents for query expansion becomes challenging. By assuming time as an effective parameter on the blog posts content, we propose a time-based query expansion method. In this method, we select terms for expansion using most relevant days for the query, as opposed to most relevant documents. This provide us with more trustable terms for expansion. Our preliminary experiments on Blog08 collection shows that this method can outperform state of the art relevance feedback methods in blog retrieval.

Recently, user generated data is growing rapidly and becoming one of the most important source of information in the web. Blogosphere (the collection of blogs on the web) is one of the main source of information in this category. In my work for my PhD, I mainly focussed on the blog distillation task which is: given a user query find the blogs that are most related to the query topic [3].

This paper addresses the blog distillation problem. That is, given a user query find the blogs most related to the query topic. We model the blogosphere as a single graph that includes extra information besides the content of the posts. By performing a random walk on this graph we extract most relevant blogs for each query. Our experiments on the TREC'07 data set show 15% improvement in MAP and 8% improvement in [email protected] over the Language Modeling baseline.