Time-based language models

Published 3 November 2003

Xiaoyan Li, W. Bruce Croft

Citations272

TL;DR

It is shown how time can be incorporated into both query-likelihood models and relevance models, and shows that time-based models perform as well as or better than the best of the heuristic techniques.

Abstract

We explore the relationship between time and relevance using TREC ad-hoc queries. A type of query is identified that favors very recent documents. We propose a time-based language model approach to retrieval for these queries. We show how time can be incorporated into both query-likelihood models and relevance models. These models were used for experiments comparing time-based language models to heuristic techniques for incorporating document recency in the ranking. Our results show that time-based models perform as well as or better than the best of the heuristic techniques.

Keywords

Computer Science

ACM SIGIR ForumA Language Modeling Approach to Information Retrieval

2,532 Citations2017Jay Ponte, W. Bruce Croft

It will be shown that probabilistic methods can be used to predict topic changes in the context of the task of new event detection and provide further proof of concept for the use of language models for retrieval tasks.

A language modeling approach to information retrieval

1,723 Citations1998Jay Ponte, W. Bruce Croft

This work proposes an approach to retrieval based on probabilistic language modeling and integrates document indexing and document retrieval into a single model, which significantly outperforms standard tf.idf weighting on two different collections and query sets.

ACM SIGIR ForumRelevance-Based Language Models

1,449 Citations2017Victor Lavrenko, W. Bruce Croft

This work proposes a novel technique for estimating a relevance model with no training data and demonstrates that it can produce highly accurate relevance models, addressing important notions of synonymy and polysemy.

Relevance based language models

1,024 Citations2001Victor Lavrenko, W. Bruce Croft

ACM SIGIR ForumDocument Language Models, Query Models, and Risk Minimization for Information Retrieval

774 Citations2017John Lafferty, ChengXiang Zhai

A framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory is presented and an operational retrieval model that extends recent developments in the language modeling approach to information retrieval is suggested.

Document language models, query models, and risk minimization for information retrieval

525 Citations2001John Lafferty, ChengXiang Zhai

A hidden Markov model information retrieval system

469 Citations1999David R. Miller, Tim Leek +1 more

A novel method for performing blind feedback in the HMM framework, a more complex HMM that models bigram production, and several other algorithmic re nements form a state-of-the-art retrieval system that ranked among the best on the TREC-7 ad hoc retrieval task.

View PDF

Zenodo (CERN European Organization for Nuclear Research)Using Language Models for Information Retrieval

454 Citations2001Djoerd Hiemstra

Experimental results on three standard tasks show that the language model-based algorithms work as well as, or better than, today's top-performing retrieval algorithms.

View PDF

The Importance of Prior Probabilities for Entry Page Search

268 Citations2002Wessel Kraaij, Thijs Westerveld +1 more

Three non-content features of web pages are explored: page length, number of incoming links and URL form, which proved to be a good predictor of entry page search results.

Temporal summaries of new topics

256 Citations2001James Allan, Rahul Gupta +1 more

It is shown that simple approaches are effective, but that the problem is far from solved, and several methods for constructing temporal summaries are proposed and evaluated.