Hierarchical dirichlet trees for information retrieval

Gholamreza Haffari; Yee Whye Teh

Hierarchical dirichlet trees for information retrieval

Gholamreza Haffari, Yee Whye Teh

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

3 Citations (Scopus)

Abstract

We propose a principled probabilisitc framework which uses trees over the vocabulary to capture similarities among terms in an information retrieval setting. This allows the retrieval of documents based not just on occurrences of specific query terms, but also on similarities between terms (an effect similar to query expansion). Additionally our principled generative model exhibits an effect similar to inverse document frequency. We give encouraging experimental evidence of the superiority of the hierarchical Dirichlet tree compared to standard baselines.

Original language	English
Title of host publication	NAACL HLT 2009 - Human Language Technologies
Subtitle of host publication	The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Pages	173-181
Number of pages	9
Publication status	Published - 1 Dec 2009
Event	Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2009 - Boulder, CO, United States of America Duration: 31 May 2009 → 5 Jun 2009

Conference

Conference	Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2009
Country	United States of America
City	Boulder, CO
Period	31/05/09 → 5/06/09

Access to Document

Link to publication in Scopus

Cite this

@inproceedings{ce6b4b89f0634cdab9c4523f1c0660d0,

title = "Hierarchical dirichlet trees for information retrieval",

abstract = "We propose a principled probabilisitc framework which uses trees over the vocabulary to capture similarities among terms in an information retrieval setting. This allows the retrieval of documents based not just on occurrences of specific query terms, but also on similarities between terms (an effect similar to query expansion). Additionally our principled generative model exhibits an effect similar to inverse document frequency. We give encouraging experimental evidence of the superiority of the hierarchical Dirichlet tree compared to standard baselines.",

author = "Gholamreza Haffari and Teh, {Yee Whye}",

year = "2009",

month = dec,

day = "1",

language = "English",

isbn = "9781932432411",

pages = "173--181",

booktitle = "NAACL HLT 2009 - Human Language Technologies",

note = "Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2009 ; Conference date: 31-05-2009 Through 05-06-2009",

}

Haffari, G & Teh, YW 2009, Hierarchical dirichlet trees for information retrieval. in NAACL HLT 2009 - Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Conference. pp. 173-181, Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2009, Boulder, CO, United States of America, 31/05/09.

Hierarchical dirichlet trees for information retrieval. / Haffari, Gholamreza; Teh, Yee Whye.

NAACL HLT 2009 - Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Conference. 2009. p. 173-181.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Hierarchical dirichlet trees for information retrieval

AU - Haffari, Gholamreza

AU - Teh, Yee Whye

PY - 2009/12/1

Y1 - 2009/12/1

N2 - We propose a principled probabilisitc framework which uses trees over the vocabulary to capture similarities among terms in an information retrieval setting. This allows the retrieval of documents based not just on occurrences of specific query terms, but also on similarities between terms (an effect similar to query expansion). Additionally our principled generative model exhibits an effect similar to inverse document frequency. We give encouraging experimental evidence of the superiority of the hierarchical Dirichlet tree compared to standard baselines.

AB - We propose a principled probabilisitc framework which uses trees over the vocabulary to capture similarities among terms in an information retrieval setting. This allows the retrieval of documents based not just on occurrences of specific query terms, but also on similarities between terms (an effect similar to query expansion). Additionally our principled generative model exhibits an effect similar to inverse document frequency. We give encouraging experimental evidence of the superiority of the hierarchical Dirichlet tree compared to standard baselines.

UR - http://www.scopus.com/inward/record.url?scp=84858373949&partnerID=8YFLogxK

M3 - Conference Paper

AN - SCOPUS:84858373949

SN - 9781932432411

SP - 173

EP - 181

BT - NAACL HLT 2009 - Human Language Technologies

T2 - Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2009

Y2 - 31 May 2009 through 5 June 2009

ER -