Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia

M. Atif Qureshi; Colm O'Riordan; Gabriella Pasi

doi:10.1145/2396761.2398680

Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia

M. Atif Qureshi
, Colm O'Riordan
, Gabriella Pasi

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

Abstract

Finding domain specific key terms/phrases from a given set of documents is a challenging task. A domain may be defined as an area of interest over a collection of documents which may not be explicitly defined but implicitly observable in those documents. When considering a collection of documents related to academic research, examples of key terms/phrases may be Information Retrieval", "Marine Biology", etc. In this paper a technique for extracting important key terms/phrases in a considered topical domain is proposed using external evidence from the titles of Wikipedia articles and the Wikipedia category graph. We performed some experiments over the document collection of Web sites of different post-graduate schools. Our preliminary evaluations show promising results for the detection of domain specific key terms/phrases from the given set of domain focused Web pages.

Original language	English
Title of host publication	CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages	2515-2518
Number of pages	4
DOIs	https://doi.org/10.1145/2396761.2398680
Publication status	Published - 2012
Externally published	Yes
Event	21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States Duration: 29 Oct 2012 → 2 Nov 2012

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Country/Territory	United States
City	Maui, HI
Period	29/10/12 → 2/11/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 14 Life Below Water

Keywords

community detection
n-gram model
open-domain knowledge
wikipedia

Access to Document

10.1145/2396761.2398680

Cite this

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title = "Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia",

abstract = "Finding domain specific key terms/phrases from a given set of documents is a challenging task. A domain may be defined as an area of interest over a collection of documents which may not be explicitly defined but implicitly observable in those documents. When considering a collection of documents related to academic research, examples of key terms/phrases may be Information Retrieval{"}, {"}Marine Biology{"}, etc. In this paper a technique for extracting important key terms/phrases in a considered topical domain is proposed using external evidence from the titles of Wikipedia articles and the Wikipedia category graph. We performed some experiments over the document collection of Web sites of different post-graduate schools. Our preliminary evaluations show promising results for the detection of domain specific key terms/phrases from the given set of domain focused Web pages.",

keywords = "community detection, n-gram model, open-domain knowledge, wikipedia",

author = "Qureshi, \{M. Atif\} and Colm O'Riordan and Gabriella Pasi",

year = "2012",

doi = "10.1145/2396761.2398680",

language = "English",

isbn = "9781450311564",

series = "ACM International Conference Proceeding Series",

pages = "2515--2518",

booktitle = "CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management",

note = "21st ACM International Conference on Information and Knowledge Management, CIKM 2012 ; Conference date: 29-10-2012 Through 02-11-2012",

}

Qureshi, MA, O'Riordan, C & Pasi, G 2012, Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia. in CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM International Conference Proceeding Series, pp. 2515-2518, 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, HI, United States, 29/10/12. https://doi.org/10.1145/2396761.2398680

Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia. / Qureshi, M. Atif; O'Riordan, Colm; Pasi, Gabriella.
CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012. p. 2515-2518 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia

AU - Qureshi, M. Atif

AU - O'Riordan, Colm

AU - Pasi, Gabriella

PY - 2012

Y1 - 2012

N2 - Finding domain specific key terms/phrases from a given set of documents is a challenging task. A domain may be defined as an area of interest over a collection of documents which may not be explicitly defined but implicitly observable in those documents. When considering a collection of documents related to academic research, examples of key terms/phrases may be Information Retrieval", "Marine Biology", etc. In this paper a technique for extracting important key terms/phrases in a considered topical domain is proposed using external evidence from the titles of Wikipedia articles and the Wikipedia category graph. We performed some experiments over the document collection of Web sites of different post-graduate schools. Our preliminary evaluations show promising results for the detection of domain specific key terms/phrases from the given set of domain focused Web pages.

AB - Finding domain specific key terms/phrases from a given set of documents is a challenging task. A domain may be defined as an area of interest over a collection of documents which may not be explicitly defined but implicitly observable in those documents. When considering a collection of documents related to academic research, examples of key terms/phrases may be Information Retrieval", "Marine Biology", etc. In this paper a technique for extracting important key terms/phrases in a considered topical domain is proposed using external evidence from the titles of Wikipedia articles and the Wikipedia category graph. We performed some experiments over the document collection of Web sites of different post-graduate schools. Our preliminary evaluations show promising results for the detection of domain specific key terms/phrases from the given set of domain focused Web pages.

KW - community detection

KW - n-gram model

KW - open-domain knowledge

KW - wikipedia

UR - https://www.scopus.com/pages/publications/84871039416

U2 - 10.1145/2396761.2398680

DO - 10.1145/2396761.2398680

M3 - Conference contribution

AN - SCOPUS:84871039416

SN - 9781450311564

T3 - ACM International Conference Proceeding Series

SP - 2515

EP - 2518

BT - CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management

T2 - 21st ACM International Conference on Information and Knowledge Management, CIKM 2012

Y2 - 29 October 2012 through 2 November 2012

ER -

Short-text domain specific key terms/phrases extraction using an n-gram model with Wikipedia

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Fingerprint

Cite this