86 citations · 100 across the 5 of their papers we have counts for
6 papers · 1 filter
Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction
Suchana Datta, Debasis Ganguly, Derek Greene +1
Motivated by the recent success of end-to-end deep neural models for ranking tasks, we present here a supervised end-to-end neural approach for query performance prediction (QPP).…
An Analysis of Variations in the Effectiveness of Query Performance Prediction
Debasis Ganguly, Suchana Datta, Mandar Mitra +1
A query performance predictor estimates the retrieval effectiveness of an IR system for a given query. An important characteristic of QPP evaluation is that, since the ground truth…
Re-evaluating the need for Modelling Term-Dependence in Text Classification Problems
Sounak Banerjee, Prasenjit Majumder, Mandar Mitra
A substantial amount of research has been carried out in developing machine learning algorithms that account for term dependence in text classification. These algorithms offer acce…
Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval
Dwaipayan Roy, Debasis Ganguly, Mandar Mitra +1
A major difficulty in applying word vector embeddings in IR is in devising an effective and efficient strategy for obtaining representations of compound units of text, such as whol…
Using Word Embeddings for Automatic Query Expansion
Dwaipayan Roy, Debjyoti Paul, Mandar Mitra +1
In this paper a framework for Automatic Query Expansion (AQE) is proposed using distributed neural language model word2vec. Using semantic and contextual relation in a distributed…
Query Expansion Using Term Distribution and Term Association
Dipasree Pal, Mandar Mitra, Kalyankumar Datta
Good term selection is an important issue for an automatic query expansion (AQE) technique. AQE techniques that select expansion terms from the target corpus usually do so in one o…