In this paper, we propose an approach to locating queryoriented experts in Microblog. We first define the experts by social influence and content relevance. Then, we adopt the BM25 model to calculate the content relevance of each account. For the social influence, we present a global-ranking algorithm as GUserRank and a topic-ranking algorithm as TUserRank after applying the LDA topic model. Af...

The SMERP 2017 data challenge track given a set of tweets posted during Italy earthquake. For retrieving more relevance information respect to user interest profile in this paper provide BM25 and word2vec techniques for retrieving relevance information from twitter stream. This techniques aim is to find real-world and most relevance information respect to the query. For retrieving most relevant...

This paper presents an information retrieval method based on a two stages indexing. The objective of this work is to analyze the impact of refining indexing and search on a homogeneous sub-collection in the quality of the results. We evaluate the impact of this approach in terms of precision using okapi BM25 and TF-IDF models on TREC-7 and TREC-8 ad hoc collections. The results show that this m...

The goals of CSIRO’s participation in the Enterprise track were formed by the nature of the tasks. With the expert finding search task, we sought to use a variety of means to associate topical expertise with individuals previously located within the collection. With the document search task, we were primarily interested in exploring issues of result diversity based on different characterisation...

The previous work has justified the assumption that document ranking can be improved by further considering the coarse-grained relations in various linguistic levels (e.g., lexical, syntactical and semantic). To the best of our knowledge, little work is reported to incorporate the fine-grained ontological relations (e.g., ) in document ranking. Two contributions are wo...

In this paper we describe our participation in CLEF-IP 2009 (prior art search task). This was the first year of the task and we focused on how to build effectively a prior art query from a patent. Basically, we implemented simple strategies to extract terms from some textual fields of the patent documents and gave more weight to title terms. We ran experiments with the well-known BM25 model. Al...

In this paper, we describe and analyze our participation in the WikipediaMM task at CLEF 2009. Our main efforts concern the expansion of the image metadata from the Wikipedia abstracts collection DBpedia. In our experiments, we use the Okapi feedback algorithm for document expansion. Compared with our text retrieval baseline, our best document expansion RUN improves MAP by 17.89%. As one of our...

The paper describes our participation in monolingual tasks at CLEF 2006. We have submitted results for the following languages: English, French, Portuguese and Hungarian. We focused on studying different weighting schemes (okapi and dfr) and retrieval strategies (passage retrieval and document retrieval) to improve retrieval performance. After an analysis of our experiments and of the official ...

This paper presents work on document retrieval for Italian carried out at ITC-irst. Two different approaches to information retrieval were investigated, one based on the Okapi weighting formula and one based on a statistical model. Development experiments were carried out using the Italian sample of the TREC-8 CLIR track. Performance evaluation was done on the Cross Language Evaluation Forum (C...