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...

Ranking documents in terms of their relevance to a given query is fundamental to many real-life applications such as document retrieval and recommendation systems. Extensive studies in this area have focused on developing efficient ranking models. While ranking models are usually trained based on given training datasets, besides model training algorithms, the quality of the document features se...

The ubiquity of the multimedia has raised a need for the system that can store, manage, structured the multimedia data in such a way that it can be retrieved intelligently. One of the current issues in media management or data mining research is ranking of retrieved documents. Ranking is one of the provocative problems for information retrieval systems. Given a user query comes up with the mill...

Within specific domains, users generally face the challenge to populate an ontology according to their needs. Especially in case of novelty detection and forecast, the user wants to integrate novel information contained in natural text documents into his/her own ontology in order to utilise the knowledge base in a further step. In this paper, a semantic document ranking approach is proposed whi...

A fundamental goal of search engines is to identify, given a query, documents that have relevant text. This is intrinsically difficult because the query and the document may use different vocabulary, or the document may contain query words without being relevant. We investigate neural word embeddings as a source of evidence in document ranking. We train a word2vec embedding model on a large unl...

Document structure weighting is a technique whereby different parts of a document (title, abstract, etc.) contribute unevenly to the overall document weight during ranking. Near optimal weights can be learned with a GA. Doing so shows a statistically significant 5% relative improvement in MAP for vector space inner product and Croft’s probabilistic ranking, but no improvement for BM25. Two appl...

The iTrust system is a decentralized and distributed system for publication, search and retrieval of information over the Internet and the Web, that is designed to make it difficult to censor or filter information. In the distributed ranking algorithm for iTrust presented in this paper, a source node that publishes a document indexes the words in the document and produces a term-frequency table...

This paper describes our participation in the GeoCLEF monolingual English task of the Cross Language Evaluation Forum 2006. Our retrieval system consists of four modules: the geographic knowledge base; the indexing module; the document retrieval module and the ranking module. The geographic knowledge base provides information about important geographic entities around the world and relationship...