Extracting significant words from corpora for ontology extraction

Dileep Damle; Victoria Uren

doi:10.1145/1088622.1088661

Extracting significant words from corpora for ontology extraction

Dileep Damle, Victoria Uren

Operations and Service Management

Open University

Research output: Chapter in Book/Published conference output › Conference publication

8 Citations (Scopus)

Abstract

We show a new method for term extraction from a domain relevant corpus using natural language processing for the purposes of semi-automatic ontology learning. Literature shows that topical words occur in bursts. We find that the ranking of extracted terms is insensitive to the choice of population model, but calculating frequencies relative to the burst size rather than the document length in words yields significantly different results.

Original language	English
Title of host publication	Proceedings of the 3rd international conference on knowledge capture - K-CAP '05
Place of Publication	New York, NY (US)
Publisher	ACM
Pages	187-188
Number of pages	2
ISBN (Print)	1-59593-163-5
DOIs	https://doi.org/10.1145/1088622.1088661
Publication status	Published - 2 Oct 2005
Event	3rd international conference on Knowledge capture - Alberta, Canada Duration: 2 Oct 2005 → 5 Oct 2005

Conference

Conference	3rd international conference on Knowledge capture
Abbreviated title	K-CAP'05
Country/Territory	Canada
City	Alberta
Period	2/10/05 → 5/10/05

Access to Document

10.1145/1088622.1088661

Cite this

@inproceedings{0b5a5c1fb9d74517940a7f5cbd3699b2,

title = "Extracting significant words from corpora for ontology extraction",

abstract = "We show a new method for term extraction from a domain relevant corpus using natural language processing for the purposes of semi-automatic ontology learning. Literature shows that topical words occur in bursts. We find that the ranking of extracted terms is insensitive to the choice of population model, but calculating frequencies relative to the burst size rather than the document length in words yields significantly different results.",

author = "Dileep Damle and Victoria Uren",

year = "2005",

month = oct,

day = "2",

doi = "10.1145/1088622.1088661",

language = "English",

isbn = "1-59593-163-5",

pages = "187--188",

booktitle = "Proceedings of the 3rd international conference on knowledge capture - K-CAP '05",

publisher = "ACM",

address = "United States",

note = "3rd international conference on Knowledge capture, K-CAP'05 ; Conference date: 02-10-2005 Through 05-10-2005",

}

TY - GEN

T1 - Extracting significant words from corpora for ontology extraction

AU - Damle, Dileep

AU - Uren, Victoria

PY - 2005/10/2

Y1 - 2005/10/2

N2 - We show a new method for term extraction from a domain relevant corpus using natural language processing for the purposes of semi-automatic ontology learning. Literature shows that topical words occur in bursts. We find that the ranking of extracted terms is insensitive to the choice of population model, but calculating frequencies relative to the burst size rather than the document length in words yields significantly different results.

AB - We show a new method for term extraction from a domain relevant corpus using natural language processing for the purposes of semi-automatic ontology learning. Literature shows that topical words occur in bursts. We find that the ranking of extracted terms is insensitive to the choice of population model, but calculating frequencies relative to the burst size rather than the document length in words yields significantly different results.

UR - http://dl.acm.org/citation.cfm?doid=1088622.1088661

U2 - 10.1145/1088622.1088661

DO - 10.1145/1088622.1088661

M3 - Conference publication

SN - 1-59593-163-5

SP - 187

EP - 188

BT - Proceedings of the 3rd international conference on knowledge capture - K-CAP '05

PB - ACM

CY - New York, NY (US)

T2 - 3rd international conference on Knowledge capture

Y2 - 2 October 2005 through 5 October 2005

ER -

Extracting significant words from corpora for ontology extraction

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Browsing for information by highlighting automatically generated annotations: a user study and evaluation

Cite this