NDLI: On collocations and topic models

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ACM Transactions on Speech and Language Processing (TSLP) : Volume 10

Issue 4, December 2013

Issue 3(Special issue on multiword expressions: From theory to practice and use, part 2), July 2013

Combining compound recognition and PCFG-LA parsing with word lattices and conditional random fields

Word sense and semantic relations in noun compounds

On collocations and topic models

A computational model of logical metonymy

Sentiment profiles of multiword expressions in test-taker essays: The case of noun-noun compounds

Semantic interpretation of noun compounds using verbal and other paraphrases

Issue 2(Special issue on multiword expressions: From theory to practice and use, part 1), June 2013

Issue 1, March 2013

ACM Transactions on Speech and Language Processing (TSLP) : Volume 9

ACM Transactions on Speech and Language Processing (TSLP) : Volume 8

ACM Transactions on Speech and Language Processing (TSLP) : Volume 7

ACM Transactions on Speech and Language Processing (TSLP) : Volume 6

ACM Transactions on Speech and Language Processing (TSLP) : Volume 5

ACM Transactions on Speech and Language Processing (TSLP) : Volume 4

ACM Transactions on Speech and Language Processing (TSLP) : Volume 3

ACM Transactions on Speech and Language Processing (TSLP) : Volume 2

ACM Transactions on Speech and Language Processing (TSLP) : Volume 1

On collocations and topic models

Content Provider	ACM Digital Library
Author	Lau, Jey Han Baldwin, Timothy Newman, David
Copyright Year	2013
Abstract	We investigate the impact of preextracting and tokenizing bigram collocations on topic models. Using extensive experiments on four different corpora, we show that incorporating bigram collocations in the document representation creates more parsimonious models and improves topic coherence. We point out some problems in interpreting test likelihood and test perplexity to compare model fit, and suggest an alternate measure that penalizes model complexity. We show how the Akaike information criterion is a more appropriate measure, which suggests that using a modest number (up to 1000) of top-ranked bigrams is the optimal topic modelling configuration. Using these 1000 bigrams also results in improved topic quality over unigram tokenization. Further increases in topic quality can be achieved by using up to 10,000 bigrams, but this is at the cost of a more complex model. We also show that multiword (bigram and longer) named entities give consistent results, indicating that they should be represented as single tokens. This is the first work to explicitly study the effect of $\textit{n}-gram$ tokenization on LDA topic models, and the first work to make empirical recommendations to topic modelling practitioners, challenging the standard practice of unigram-based tokenization.
Starting Page	1
Ending Page	14
Page Count	14
File Format	PDF
ISSN	15504875
e-ISSN	15504883
DOI	10.1145/2483969.2483972
Volume Number	10
Issue Number	3
Journal	ACM Transactions on Speech and Language Processing (TSLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-07-11
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	1700/1701 Computational Mathematics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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