Clustering is an unsupervised classification of patterns(observations,data items,or feature vectors) into groups(clusters).So far,many clustering algorithms have been proposed.With the rapid development of internet,short texts such as query logs and Twitter messages play a more and more important role in our daily life.Most existing clustering methods are hard to be applied in dealing with this kind of information due to the huge scale of data.This paper reveals the long tail distribution of this kind of information,and proposes an incomplete clustering algorithm.The experimental results show that the proposed method can cluster the short texts effectively and efficiently.

: Web Spamming refers those web pages deceive search engines so as to get a higher rank in their search result. We work on the data set TrecWeb09, based on a content-based spamming classifier, to check the two ends of a hyperlink; if the two end pages either is content spamming, or both are not so good, then the hyperlink will be discarded. After all hyperlinks have been checked, PageRank value shall be re-count on the re-built web network. The balance of one page's PageRank value will be regarded as its link spamming. Then the link spamming score and the result of content deceiving analyzer will be combined as the final estimation of one page's spamming.

This paper describes our participation in blog track of TREC2009. All runs are submitted for both two task, namely Top stories identification task and faceted blog distillation task. The “FirteX” platform was used to index and retrieval posts. As for top stories identification task, to identify important headlines, we measure the importance of headline by accumulating the BM25 relevance score with posts on the query day. We propose a graph-based iterative approach and a sub-topic detecting based approach respectively to identify diverse blog posts. As for faceted blog distillation task: we adopt a very straightforward approach and measure the topical relevance by only exploiting top ad-hoc 10000 posts. To identify facet inclination, we either train centroid classifier or compute facet inclination weights of terms to compute facet inclination score and rerank feed by combining relevance score and facet inclination score.

This paper is about the work done for ad-hoc task of TREC 2009 Web Track. We introduce three methods for this task, including two improved BM25 models and query expansion. The results of these models indicate that both minimum window and query expansion could improve BM25 model.

We (ICTNET team) participated in Web Track of TREC2009, and in this paper, we summarize our work on Diversity task of Web Track, which is new in this year. The goal of the diversity task is to return a ranked list of pages that together provide complete coverage for a query, while avoiding excessive redundancy in the result list. For this task, we cluster the results of ad hoc task, and rerank the results depend on subtopics docs covers. Besides, we introduce two methods which tried to find the implicit subtopic by using the docs returned from commerce search engine.