This is an overview of the NTCIR-9 INTENT task, which comprises the Subtopic Mining and the Document Ranking subtasks. The INTENT task attracted participating teams from seven different countries/regions – 16 teams for Subtopic Mining and 8 teams for Document Ranking. The Subtopic Mining subtask received 42 Chinese runs and 14 Japanese runs; the Document Ranking subtask received 24 Chinese runs...

In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place edges between documents based on their similarity. Given a query, we compute similarity of the query with respect to every document in the graph. Based on these similarity values, documents are ranked for a given query. Initially,...

Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and during the last few years enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and...

This paper regards the query keywords selection problem in source retrieval as learning a ranking model to choose the method of keywords extraction over suspicious document segments. Four basic methods are used in our ranking function: BM25, TFIDF, TF and EW. Then, a ranking model based on Ranking SVM is proposed to rank the query keywords group which is contributed to get the higher evaluation...

Text readability is typically defined in terms of “grade level”; the expected educational level of the reader at which the text is directed. Mechanisms for measuring readability in English documents are well established; however this is not in case in many other languages, such as syllabic alphabetic languages. In this paper seven different mechanisms for assessing the readability of syllabic a...

Aggregate ranking tasks are those where documents are not the final ranking outcome, but instead an intermediary component. For instance, in expert search, a ranking of candidate persons with relevant expertise to a query is generated after consideration of a document ranking. Many models exist for aggregate ranking tasks, however obtaining an effective and robust setting for different aggregat...

This paper provides an overview of the NTCIR-10 INTENT-2 task (the second INTENT task), which comprises the Subtopic Mining and the Document Ranking subtasks. INTENT-2 attracted participating teams from China, France, Japan and South Korea – 12 teams for Subtopic Mining and 4 teams for Document Ranking (including an organisers’ team). The Subtopic Mining subtask received 34 English runs, 23 Chi...

Learning To Rank (LTR) techniques aim to learn an effective document ranking function by combining several document features. While the function learned may be uniformly applied to all queries, many studies have shown that different ranking functions favour different queries, and the retrieval performance can be significantly enhanced if an appropriate ranking function is selected for each indi...

In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from data management, algorithms, information retrieval, and recommender systems communities. this survey, we give a systematic overview of work, offering broad perspective that connects formalizations approaches across sub-fields. An important contribution ...