This paper describes our approaches at NTCIR-12 short text conversation (STC) task (Chinese). For a new post, instead of considering post-comment similarity, our system focus on finding similar posts in the repository and retrieve their corresponding comments. Meanwhile, we choose frequency property of comments to adjust ranking models. Our best run achieves 0.4854 for mean P, 0.3367 for mean n...

This paper reports on the participation of the University of Padua to the TREC 2013 Federated Web Search track. The objective was the experimental investigation in Federated Web Search setting of TWF·IRF, which is a recursive weighting scheme for resource selection. The experimental results show that the TWF component, that is peculiar of this scheme, is sufficient to obtain an effective search...

In TREC 2009, we participate in the Web track, and focus on the diversity task. We propose to diversify web search results by first mining subtopics, and then rank results based on mined subtopics. We propose a model to diversify search results by considering both relevance of documents and richness of mined subtopics. Our experimental results show that the model improves diversity of search re...

We describe our approach in identifying documents in a specific collection that are able to sufficiently answer a series of geotemporal queries. This year, we submitted four runs for the NTCIR-9 GeoTime task, using the Indri search engine, on a collection of English-language newspaper articles. Our four submitted runs achieved nDCG scores ranging from 0.5275 to 0.6254 and MAP ranging from 0.416...

We present the results of applying a learning to rank algorithm to the 2011 TREC Legal dataset. The learning to rank algorithm we use was designed to maximize NDCG, MAP, and AUC scores. We therefore examine our results using the AUC and hypothetical F1 scores. We find query expansion and learning to rank improve scores beyond standard language model retrieval, however learning to rank does not ...

Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank problem in information retrieval. We consider popular listwise performance measures such as Normalized Discounted Cumulative Gain (NDCG) and Average Precision (AP). A modern perspective on perceptron for classification i...

Users express their information needs in terms of queries in search engines to find some relevant documents on the Internet. However, search queries are usually short, ambiguous and/or underspecified. To understand user’s search intent, subtopic mining plays an important role and has attracted attention in the recent years. In this paper, we describe our approach to identifying, and then rankin...

To perform online inference efficiently, hashing techniques, devoted to encoding model parameters as binary codes, play a key role in reducing the computational cost of content-aware recommendation (CAR), particularly on devices with limited computation resource. However, current methods for CAR fail align their learning objectives (e.g., squared loss) ranking-based metrics Normalized Discounte...

Sensing Microblog from retrieval and summarization become the challenging area for the Information retrieval community. Twitter is one of the most popular micro blogging platforms. In this paper, Twitter posts called tweets are studied from retrieval and extractive summarization perspectives. Given a set of topics or interest profiles or information requirement, a Microblog summarization system...