Topic based language models for ad hoc information retrieval

L. Azzopardi; M. Girolami; C.J. van Rijsbergen

doi:10.1109/IJCNN.2004.1381205

Topic based language models for ad hoc information retrieval

L. Azzopardi^*, M. Girolami, C.J. van Rijsbergen

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

40 Citations (Scopus)

24 Downloads (Pure)

Abstract

We propose a topic based approach to language modelling for ad-hoc Information Retrieval (IR). Many smoothed estimators used for the multinomial query model in IR rely upon the estimated background collection probabilities. In this paper, we propose a topic based language modelling approach, that uses a more informative prior based on the topical content of a document. In our experiments, the proposed model provides comparable IR performance to the standard models, but when combined in a two stage language model, it outperforms all other estimated models.

Original language	English
Title of host publication	2004 IEEE International Joint Conference on Neural Networks
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	3281-3286
Number of pages	6
ISBN (Print)	0780383591
DOIs	https://doi.org/10.1109/IJCNN.2004.1381205
Publication status	Published - 17 Jan 2005
Event	2004 IEEE International Joint Conference on Neural Networks - Budapest, Hungary Duration: 25 Jul 2004 → 29 Jul 2004

Conference

Conference	2004 IEEE International Joint Conference on Neural Networks
Country/Territory	Hungary
City	Budapest
Period	25/07/04 → 29/07/04

Keywords

language modelling
Information retrieval
two stage language model

Access to Document

10.1109/IJCNN.2004.1381205

Azzopardi-etal-IEEE-IJCNN-2004-Topic-based-language-models-for-ad-hoc-information-retrievalAccepted author manuscript, 93.6 KB

Cite this

@inproceedings{8b99325a24f74c86977399772d58b34c,

title = "Topic based language models for ad hoc information retrieval",

abstract = "We propose a topic based approach to language modelling for ad-hoc Information Retrieval (IR). Many smoothed estimators used for the multinomial query model in IR rely upon the estimated background collection probabilities. In this paper, we propose a topic based language modelling approach, that uses a more informative prior based on the topical content of a document. In our experiments, the proposed model provides comparable IR performance to the standard models, but when combined in a two stage language model, it outperforms all other estimated models.",

keywords = "language modelling, Information retrieval, two stage language model",

author = "L. Azzopardi and M. Girolami and {van Rijsbergen}, C.J.",

year = "2005",

month = jan,

day = "17",

doi = "10.1109/IJCNN.2004.1381205",

language = "English",

isbn = "0780383591",

pages = "3281--3286",

booktitle = "2004 IEEE International Joint Conference on Neural Networks",

publisher = "IEEE",

note = "2004 IEEE International Joint Conference on Neural Networks ; Conference date: 25-07-2004 Through 29-07-2004",

}

TY - GEN

T1 - Topic based language models for ad hoc information retrieval

AU - Azzopardi, L.

AU - Girolami, M.

AU - van Rijsbergen, C.J.

PY - 2005/1/17

Y1 - 2005/1/17

N2 - We propose a topic based approach to language modelling for ad-hoc Information Retrieval (IR). Many smoothed estimators used for the multinomial query model in IR rely upon the estimated background collection probabilities. In this paper, we propose a topic based language modelling approach, that uses a more informative prior based on the topical content of a document. In our experiments, the proposed model provides comparable IR performance to the standard models, but when combined in a two stage language model, it outperforms all other estimated models.

AB - We propose a topic based approach to language modelling for ad-hoc Information Retrieval (IR). Many smoothed estimators used for the multinomial query model in IR rely upon the estimated background collection probabilities. In this paper, we propose a topic based language modelling approach, that uses a more informative prior based on the topical content of a document. In our experiments, the proposed model provides comparable IR performance to the standard models, but when combined in a two stage language model, it outperforms all other estimated models.

KW - language modelling

KW - Information retrieval

KW - two stage language model

UR - http://www.scopus.com/inward/record.url?scp=10944265361&partnerID=8YFLogxK

UR - https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9486

U2 - 10.1109/IJCNN.2004.1381205

DO - 10.1109/IJCNN.2004.1381205

M3 - Conference contribution book

AN - SCOPUS:10944265361

SN - 0780383591

SP - 3281

EP - 3286

BT - 2004 IEEE International Joint Conference on Neural Networks

PB - IEEE

CY - Piscataway, NJ

T2 - 2004 IEEE International Joint Conference on Neural Networks

Y2 - 25 July 2004 through 29 July 2004

ER -

Topic based language models for ad hoc information retrieval

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this