The probability that a term appears in relevant documents ( ) is a fundamental quantity in several probabilistic retrieval models, however it is difficult to estimate without rele...
In classic InformationRetrieval systems a relevant document will not be retrieved in response to a query if the document and query representations do not share at least one term. T...
This paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this framework, querie...
Content-oriented retrieval models are based on a document-term matrix, whereas link-oriented retrieval models are based on an adjacent (parentchild) matrix. Term frequency and inv...
This technical note presents the system built for the IP track of CLEF 2010 based on PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS), the modular search infrastruct...
In the robust track, we mainly tested our passage-based retrieval model with different passage sizes and weighting schemes. In our approach, we used two retrieval models, namely t...
We present a prototype system using array comprehensions to bridge the gap between databases and information retrieval. It allows researchers to express their retrieval models in t...
This paper discusses some issues related to the planning of a series of grid experiments in the context of the TrebleCLEF project with the aim of improving the comprehension of Mul...
Metasearch engines submit the user query to several underlying search engines and then merge their retrieved results to generate a single list that is more effective to the users&...
Structured documents contain elements defined by the author(s) and annotations assigned by other people or processes. Structured documents pose challenges for probabilistic retrie...