In this paper, we document our efforts in participating to the TREC 2009 Entity Ranking and Web Tracks. We had multiple aims: For the Web Track’s Adhoc task we experiment with document text and anchor text representation, and the use of the link structure. For the Web Track’s Diversity task we experiment with using a top down sliding window that, given the top ranked documents, chooses as the n...

In this paper, we present our approach in the We Want Web(WWW)[1] task of NTCIR-13, for both English and Chinese languages. We implement a ranking model for traditional re-ranking problems based on learning to rank. We first process the raw data and extract text features, match features, embedding features and semantic features for each query-document pair. Then we use LamdaMART[2] to train the...

In this paper, we document our efforts in participating to the TREC 2009 Entity Ranking and Web Tracks. We had multiple aims: For the Web Track’s Adhoc task we experiment with document text and anchor text representation, and the use of the link structure. For the Web Track’s Diversity task we experiment with using a top down sliding window that, given the top ranked documents, chooses as the n...

In this paper, we examine whether it is possible to effectively incorporate document genre features into document relevance ranking. First, a method for extracting ‘seriousness’ score of a document using canonical discriminant analysis applied to a sample of functional styles is proposed. Second, effects of aggregating genre-related and text relevance ranks are considered. Evaluation of the res...

Document summarization deals with providing condensed version of the original document. We present an extractive informative single medical document summarization approach. We compare the tokens in the sentence with cue words. A sentence ranking method is used to extract the important sentences. The existing summarizers are used for performance analysis.

In this paper, we examine whether it is possible to effectively incorporate document genre features into document relevance ranking. First, a method for extracting ‘seriousness’ score of a document using canonical discriminant analysis applied to a sample of functional styles is proposed. Second, effects of aggregating genre-related and text relevance ranks are considered. Evaluation of the res...

This is the first year that members of the Database and Information System Lab (DBIS) at University of Illinois at Chicago (UIC) participate in TREC. We participate in two tasks for the Web track: topic distillation and named page finding. Linkage information among documents as well as content information about documents is used in some of our submitted runs. We utilize the Okapi weighting sche...

This is the first year that members of the Database and Information System Lab (DBIS) at University of Illinois at Chicago (UIC) participate in TREC. We participate in two tasks for the Web track: topic distillation and named page finding. Linkage information among documents as well as content information about documents is used in some of our submitted runs. We utilize the Okapi weighting sche...

This paper proposes a method of ranking XML documents with respect to an Information Retrieval query by means of fuzzy logic. The proposed method allows imprecise queries to be evaluated against an XML document collection and it provides a model of ranking XML documents. In addition the proposed method enables sophisticated ranking of documents by employing proximity measures and the concept of...

Social networks allow users getting personalized recommendations for interesting resources like websites or scientific papers by using reviews of users they trust. Search engines rank documents by using the reference structure to compute a visibility for each document with reference structure-based functions like PageRank. Personalized document visibilities can be computed by integrating both a...