We propose a novel probabilistic method based on the Hidden Markov Model (HMM) to learn the structure of a Latent Variable Model (LVM) for query language modeling. In the proposed...
Many of the recently proposed algorithms for learning feature-based ranking functions are based on the pairwise preference framework, in which instead of taking documents in isola...
Vitor R. Carvalho, Jonathan L. Elsas, William W. C...
Query processing techniques for XML data have focused mainly on tree-pattern queries (TPQs). However, the need for querying XML data sources whose structure is very complex or not...
Large scale digitization projects have been conducted at the Internet Archive digital library to preserve cultural artifacts and to provide permanent access. The increasing amount...
For a given query raised by a specific user, the Query Suggestion technique aims to recommend relevant queries which potentially suit the information needs of that user. Due to th...
We present a passage relevance model for integrating syntactic and semantic evidence of biomedical concepts and topics using a probabilistic graphical model. Component models of t...
Structured documents contain elements defined by the author(s) and annotations assigned by other people or processes. Structured documents pose challenges for probabilistic retrie...
Generating alternative queries, also known as query suggestion, has long been proved useful to help a user explore and express his information need. In many scenarios, such sugges...