Cogprints
Login | Create Account

Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL

Turney, Peter (2001) Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. [Conference Paper]

[img]
Preview
Postscript
557Kb
[img]
Preview
PDF
47Kb

Abstract

This paper presents a simple unsupervised learning algorithm for recognizing synonyms, based on statistical data acquired by querying a Web search engine. The algorithm, called PMI-IR, uses Pointwise Mutual Information (PMI) and Information Retrieval (IR) to measure the similarity of pairs of words. PMI-IR is empirically evaluated using 80 synonym test questions from the Test of English as a Foreign Language (TOEFL) and 50 synonym test questions from a collection of tests for students of English as a Second Language (ESL). On both tests, the algorithm obtains a score of 74%. PMI-IR is contrasted with Latent Semantic Analysis (LSA), which achieves a score of 64% on the same 80 TOEFL questions. The paper discusses potential applications of the new unsupervised learning algorithm and some implications of the results for LSA and LSI (Latent Semantic Indexing).

Item Type:Conference Paper
Keywords:PMI-IR, synonyms, LSA, LSI, Latent Semantic Analysis, text mining, web mining, TOEFL, mutual information
Subjects:Computer Science > Language
Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:1796
Deposited By:Turney, Peter
Deposited On:12 Sep 2001
Last Modified:19 Dec 2009 19:18

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

1. Church, K.W., Hanks, P.: Word Association Norms, Mutual Information and Lexicography.

In: Proceedings of the 27th Annual Conference of the Association of Computational Lin-guistics,

(1989) 76-83.

2. Church, K.W., Gale, W., Hanks, P., Hindle, D.: Using Statistics in Lexical Analysis. In: Uri

Zernik (ed.), Lexical Acquisition: Exploiting On-Line Resources to Build a Lexicon. New

Jersey: Lawrence Erlbaum (1991) 115-164.

3. AltaVista, AltaVista Company, Palo Alto, California, http://www.altavista.com/.

4. Test of English as a Foreign Language (TOEFL), Educational Testing Service, Princeton,

New Jersey, http://www.ets.org/.

5. Tatsuki, D.: Basic 2000 Words - Synonym Match 1. In: Interactive JavaScript Quizzes for

ESL Students, http://www.aitech.ac.jp/~iteslj/quizzes/js/dt/mc-2000-01syn.html (1998).

6. Landauer, T.K., Dumais, S.T.: A Solution to Plato’s Problem: The Latent Semantic Analysis

Theory of the Acquisition, Induction, and Representation of Knowledge. Psychological Re-view,

104 (1997) 211-240.

7. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by

Latent Semantic Analysis. Journal of the American Society for Information Science, 41

(1990) 391-407.

8. Berry, M.W., Dumais, S.T., Letsche, T.A.: Computational Methods for Intelligent Informa-tion

Access. Proceedings of Supercomputing ’95, San Diego, California, (1995).

9. Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. Cam-bridge,

Massachusetts: MIT Press (1999).

10. Firth, J.R.: A Synopsis of Linguistic Theory 1930-1955. In Studies in Linguistic Analysis,

pp. 1-32. Oxford: Philological Society (1957). Reprinted in F.R. Palmer (ed.), Selected Pa-pers

of J.R. Firth 1952-1959, London: Longman (1968).

11. AltaVista: AltaVista Advanced Search Cheat Sheet, AltaVista Company, Palo Alto, Cali-fornia,

http://doc.altavista.com/adv_search/syntax.html (2001).

12. Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. Cambridge, Massachusetts:

MIT Press (1998). For more information: http://www.cogsci.princeton.edu/~wn/.

13. Haase, K.: Interlingual BRICO. IBM Systems Journal, 39 (2000) 589-596. For more infor-mation:

http://www.framerd.org/brico/.

14. Vossen, P. (ed.): EuroWordNet: A Multilingual Database with Lexical Semantic Networks.

Dordrecht, Netherlands: Kluwer (1998). See: http://www.hum.uva.nl/~ewn/.

15. Turney, P.D.: Learning Algorithms for Keyphrase Extraction. Information Retrieval, 2

(2000) 303-336.

16. Grefenstette, G.: Finding Semantic Similarity in Raw Text: The Deese Antonyms. In: R.

Goldman, P. Norvig, E. Charniak and B. Gale (eds.), Working Notes of the AAAI Fall Sym-posium

on Probabilistic Approaches to Natural Language. AAAI Press (1992) 61-65.

17. Schütze, H.: Word Space. In: S.J. Hanson, J.D. Cowan, and C.L. Giles (eds.), Advances in

Neural Information Processing Systems 5, San Mateo California: Morgan Kaufmann (1993)

895-902.

18. Lin, D.: Automatic Retrieval and Clustering of Similar Words. In: Proceedings of the 17th

International Conference on Computational Linguistics and 36th Annual Meeting of the As-sociation

for Computational Linguistics, Montreal (1998) 768-773.

19. Richardson, R., Smeaton, A., Murphy, J.: Using WordNet as a Knowledge Base for Meas-uring

Semantic Similarity between Words. In Proceedings of AICS Conference. Trinity

College, Dublin (1994).

20. Lee, J.H., Kim, M.H., Lee, Y.J.: Information Retrieval Based on Conceptual Distance in IS-A

Hierarchies. Journal of Documentation, 49 (1993) 188-207.

21. Resnik, P.: Semantic Similarity in a Taxonomy: An Information-Based Measure and its

Application to Problems of Ambiguity in Natural Language. Journal of Artificial Intelli-gence

Research, 11 (1998) 95-130.

22. Jiang, J., Conrath, D.: Semantic Similarity Based on Corpus Statistics and Lexical Taxon-omy.

In: Proceedings of the 10th International Conference on Research on Computational

Linguistics, Taiwan, (1997).

23. Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic Itemset Counting and Implication

Rules for Market Basket Data. In: Proceedings of the 1997 ACM-SIGMOD International

Conference on the Management of Data (1997) 255-264.

24. Sullivan, D.: Search Engine Sizes. SearchEngineWatch.com, internet.com Corporation,

Darien, Connecticut, http://searchenginewatch.com/reports/sizes.html (2000).

25. Papadimitriou, C.H., Raghavan, P., Tamaki, H., Vempala, S.: Latent Semantic Indexing: A

Probabilistic Analysis. In: Proceedings of the Seventeenth ACM-SIGACT-SIGMOD-SIGART

Symposium on Principles of Database Systems, Seattle, Washington (1998) 159-

168.

26. Sparck Jones, K.: Comparison Between TREC2 and TREC3. In: D. Harman (ed.), The

Third Text REtrieval Conference (TREC3), National Institute of Standards and Technology

Special Publication 500-226, Gaithersburg, Maryland (1994) C1-C4.

27. Buckley, C., Salton, G., Allan, J., Singhal, A.: Automatic Query Expansion Using SMART:

TREC 3. In: The Third Text REtrieval Conference (TREC3), D. Harman (ed.), National In-stitute

of Standards and Technology Special Publication 500-226, Gaithersburg, Maryland

(1994) 69-80.

Metadata

Repository Staff Only: item control page

Cogprints is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.