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Found 7 papers, 0 papers with code
ir_explain: a Python Library of Explainable IR Methods
The library is designed to make it easy to reproduce state-of-the-art ExIR baselines on standard test collections, as well as to explore new approaches to explaining IR models and methods.
On the Feasibility and Robustness of Pointwise Evaluation of Query Performance Prediction
Despite the retrieval effectiveness of queries being mutually independent of one another, the evaluation of query performance prediction (QPP) systems has been carried out by measuring rank correlation over an entire set of queries.
Explainability of Text Processing and Retrieval Methods: A Critical Survey
This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods.
Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction
In contrast to unsupervised approaches that rely on various statistics of document score distributions, our approach is entirely data-driven.
An Analysis of Variations in the Effectiveness of Query Performance Prediction
An important characteristic of QPP evaluation is that, since the ground truth retrieval effectiveness for QPP evaluation can be measured with different metrics, the ground truth itself is not absolute, which is in contrast to other retrieval tasks, such as that of ad-hoc retrieval.
Tag Embedding Based Personalized Point Of Interest Recommendation System
We model Point of Interest-based on tag embedding and also model the users (user profile) based on the Point Of Interest rated by them.
Re-evaluating the need for Modelling Term-Dependence in Text Classification Problems
In order to prove the conjecture, the performance of one of the best dependence models is compared to several well established algorithms in text classification.
