2019
Conferences
Josiane Mothe; Léa Laporte; Adrian-Gabriel Chifu
Predicting Query Difficulty in IR: Impact of Difficulty Definition Conference
2019 11th International Conference on Knowledge and Systems Engineering, KSE2019 IEEE 2019.
Abstract | Links | BibTeX | Tags: Information Retrieval, Query Difficulty Prediction, Query Features
@conference{mothe2019predicting,
title = {Predicting Query Difficulty in IR: Impact of Difficulty Definition},
author = {Josiane Mothe and Léa Laporte and Adrian-Gabriel Chifu},
url = {https://www.irit.fr/publis/SIG/2019_KSE_MLC.pdf},
year = {2019},
date = {2019-10-24},
urldate = {2019-01-01},
booktitle = {2019 11th International Conference on Knowledge and Systems Engineering},
pages = {1--6},
organization = {IEEE},
series = {KSE2019},
abstract = {While it exists information on about any topic on the web, we know from information retrieval (IR) evaluation programs that search systems fail to answer to some queries in an effective manner. System failure is associated to query difficulty in the IR literature. However, there is no clear definition of query difficulty. This paper investigates several ways of defining query difficulty and analyses the impact of these definitions on query difficulty prediction results. Our experiments show that the most stable definition across collections is a threshold-based definition of query difficulty classes.},
keywords = {Information Retrieval, Query Difficulty Prediction, Query Features},
pubstate = {published},
tppubtype = {conference}
}
While it exists information on about any topic on the web, we know from information retrieval (IR) evaluation programs that search systems fail to answer to some queries in an effective manner. System failure is associated to query difficulty in the IR literature. However, there is no clear definition of query difficulty. This paper investigates several ways of defining query difficulty and analyses the impact of these definitions on query difficulty prediction results. Our experiments show that the most stable definition across collections is a threshold-based definition of query difficulty classes.
2018
Conferences
Adrian-Gabriel Chifu; Léa Laporte; Josiane Mothe; Md Zia Ullah
Query performance prediction focused on summarized letor features Conference
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR2018 2018.
Abstract | Links | BibTeX | Tags: Letor Features, Post Retrieval Features, Query Difficulty Prediction, Query Features, Query Performance Prediction
@conference{chifu2018query,
title = {Query performance prediction focused on summarized letor features},
author = {Adrian-Gabriel Chifu and Léa Laporte and Josiane Mothe and Md Zia Ullah},
url = {ftp://ftp.irit.fr/IRIT/SIG/2018_SIGIR_CLMU.pdf},
year = {2018},
date = {2018-07-01},
urldate = {2018-01-01},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {1177--1180},
series = {SIGIR2018},
abstract = {Query performance prediction (QPP) aims at automatically estimating the information retrieval system effectiveness for any user's query. Previous work has investigated several types of pre- and post-retrieval query performance predictors; the latter has been shown to be more effective. In this paper we investigate the use of features that were initially defined for learning to rank in the task of QPP. While these features have been shown to be useful for learning to rank documents, they have never been studied as query performance predictors. We developed more than 350 variants of them based on summary functions. Conducting experiments on four TREC standard collections, we found that Letor-based features appear to be better QPP than predictors from the literature. Moreover, we show that combining the best Letor features outperforms the state of the art query performance predictors. This is the first study that considers such an amount and variety of Letor features for QPP and that demonstrates they are appropriate for this task.},
keywords = {Letor Features, Post Retrieval Features, Query Difficulty Prediction, Query Features, Query Performance Prediction},
pubstate = {published},
tppubtype = {conference}
}
Query performance prediction (QPP) aims at automatically estimating the information retrieval system effectiveness for any user's query. Previous work has investigated several types of pre- and post-retrieval query performance predictors; the latter has been shown to be more effective. In this paper we investigate the use of features that were initially defined for learning to rank in the task of QPP. While these features have been shown to be useful for learning to rank documents, they have never been studied as query performance predictors. We developed more than 350 variants of them based on summary functions. Conducting experiments on four TREC standard collections, we found that Letor-based features appear to be better QPP than predictors from the literature. Moreover, we show that combining the best Letor features outperforms the state of the art query performance predictors. This is the first study that considers such an amount and variety of Letor features for QPP and that demonstrates they are appropriate for this task.
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