A probabilistic framework for automatic term recognition

Intelligent Data Analysis

Volumn 13, Issue 4, 2009, Pages 499-539

  Wong, Wilson ^a   Liu, Wei ^a   Bennamoun, Mohammed ^a  

^a UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

Author keywords

Bayes' theorem; Term characteristic; Term extraction; Term recognition; Termhood; Word distribution models

Indexed keywords

EID: 69749095745     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-2009-0379     Document Type: Article

References (57)

1. H. Abdi, The method of least squares, in: Encyclopedia of Measurement and Statistics, N. Salkind, ed., Thousand Oaks, CA, USA, 2007.
2. G. Amati and C. van Rijsbergen, Term frequency normalization via pareto distributions. In Proceedings of the 24th BCS-IRSG European Colloquium on Information Retrieval Research, Glasgow, UK, 2002.
3. H. Baayen, Statistical models for word frequency distributions: A linguistic evaluation, Computers and the Humanities 26(5-6) (2004), 347-363.
4. R. Basili, A. Moschitti, M. Pazienza and F. Zanzotto, A contrastive approach to term extraction. In Proceedings of the 4th Terminology and Artificial Intelligence Conference (TIA), France, 2001.
5. R. Basili, M. Pazienza and F. Zanzotto. Modelling syntactic context in automatic term extraction. In Proceedings of the International Conference on Recent Advances in Natural Language Processing, Bulgaria, 2001.
6. A. Bookstein, S. Klein and T. Raita, Clumping properties of contentbearing words, Journal of the American Society of Information Science 49(2) (1998), 102-114.
7. A. Bookstein and D. Swanson, Probabilistic models for automatic indexing, Journal of the American Society for Information Science 25(5) (1974), 312-318.
8. T. Cabre-Castellvi, R. Estopa and J. Vivaldi-Palatresi, Automatic term detection: A review of current systems, in: Recent Advances in Computational Terminology, D. Bourigault, C. Jacquemin and M.L. Homme, eds, John Benjamins, 2001.
9. N. Chinchor, D. Lewis and L. Hirschman, Evaluating message understanding systems: An analysis of the third message understanding conference (muc-3), Computational Linguistics 19(3) (1993), 409-449.
10. K. Church and W. Gale, Inverse document frequency (idf): A measure of deviations from poisson. In Proceedings of the ACL 3rd Workshop on Very Large Corpora, 1995.
11. K. Church and W. Gale, Poisson mixtures, Natural Language Engineering 1(2) (1995), 163-190.
12. R. Cilibrasi and P. Vitanyi, The google similarity distance, IEEE Transactions on Knowledge and Data Engineering 19(3) (2007), 370-383.
13. N. Draper and H. Smith, Applied Regression Analysis, (3rd ed.), John Wiley & Sons, 1998.
14. H. Edmundson, Statistical inference in mathematical and computational linguistics, International Journal of Computer and Information Sciences 6(2) (1977), 95-129.
15. K. Frantzi and S. Ananiadou. Automatic term recognition using contextual cues. In Proceedings of the IJCAI Workshop on Multilinguality in Software Industry: The AI Contribution, Japan, 1997.
16. N. Fuhr, Two models of retrieval with probabilistic indexing. In Proceedings of the 9th ACM SIGIR International Conference on Research and Development in Information Retrieval, 1986.
17. N. Fuhr, Probabilistic models in information retrieval, The Computer Journal 35(3) (1992), 243-255.
18. A. Hippisley, D. Cheng and K. Ahmad, The head-modifier principle and multilingual term extraction, Natural Language Engineering 11 (2) (2005), 129-157.
19. L. Hirschman, A. Yeh, C. Blaschke and A. Valencia, Overview of biocreative: Critical assessment of information, BMC Bioinformatics 6(1) (2005), S1.
20. T. Hisamitsu, Y. Niwa and J. Tsujii, A method of measuring term representativeness: Baseline method using co-occurrence di. In Proceedings of the 18th International Conference on Computational Linguistics, Germany, 2000.
21. A. Hulth, Improved automatic keyword extraction given more linguistic knowledge. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Japan, 2003.
22. J. Izsak, Some practical aspects of fitting and testing the zipf-mandelbrot model, Scientometrics 67(1) (2006), 107-120.
23. J. Jiang and D. Conrath, Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the International Conference Research on Computational Linguistics (ROCLING), Taiwan, 1997.
24. K. Jones, S. Walker and S. Robertson, A probabilistic model of information retrieval: Development and status, Information Processing and Management 36(6) (1998), 809-840.
25. K. Kageura and B. Umino, Methods of automatic term recognition: A review, Terminology 3(2) (1996), 259-289.
26. S. Katz, Distribution of content words and phrases in text and language modelling, Natural Language Engineering 2(1) (1996), 15-59.
27. J. Kim, T. Ohta, Y. Teteisi and J. Tsujii, Genia corpus - a semantically annotated corpus for bio-textmining, Bioinformatics 19(1) (2003), 180-182.
28. C. Kit, Corpus tools for retrieving and deriving termhood evidence. In Proceedings of the 5th East Asia Forum of Terminology, Haikou, China, 2002.
29. S. LeMoigno, J. Charlet, D. Bourigault, P. Degoulet and M. Jaulent, Terminology extraction from text to build an ontology in surgical intensive care. In Proceedings of the ECAI Workshop on Machine Learning and Natural Language Processing for Ontology Engine, 2002.
30. D. Lewis, Naive (bayes) at forty: The independence assumption in information retrieval. In Proceedings of the 10th European Conference on Machine Learning, 1998.
31. N. Loukachevitch and B. Dobrov, Sociopolitical domain as a bridge from general words to terms of specific domains. In Proceedings of the 2nd International Global Wordnet Conference, 2004.
32. H. Luhn, Keyword in context index for technical literature, American Documentation 11(4) (1960), 288-295.
33. J. Makhoul, F. Kubala, R. Schwartz and R. Weischedel, Performance measures for information extraction. In Proceedings of the DARPA Broadcast News Workshop, 1999.
34. B. Mandelbrot, Information Theory and Psycholinguistics: A Theory of Word Frequencies, MIT Press, MA, USA, 1967.
35. C. Manning and H. Schutze, Foundations of Statistical Natural Language Processing, MIT Press, MA, USA, 1999.
36. E. Margulis, N-poisson document modelling. In Proceedings of the 15th ACM SIGIR International Conference on Research and Development in Information Retrieval, 1992.
37. D. Maynard and S. Ananiadou, Term extraction using a similarity-based approach. In Recent Advances in Computational Terminology, John Benjamins, 1999.
38. D. Maynard and S. Ananiadou, Identifying terms by their family and friends. In Proceedings of the 18th International Conference on Computational Linguistics, Germany, 2000.
39. R. Plackett, Some theorems in least squares, Biometrika 37(1/2) (1950), 149-157.
40. J. Rennie, Derivation of the f-measure. http://people.csail.mit.edu/ jrennie/writing/fmeasure.pdf, 2004.
41. P. Resnik, Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language, Journal of Artificial Intelligence Research 11(1) (1999), 95-130.
42. S. Robertson, Understanding inverse document frequency: On theoretical arguments for idf, Journal of Documentation 60(5) (2004), 503-520.
43. H. Simon, On a class of skew distribution functions, Biometrika 42(3-4) (1955), 425-440.
44. B. Smith, S. Penrod, A. Otto and R. Park, Jurors use of probabilistic evidence, Behavioral Science 20(1) (1996), 49-82.
45. P. Srinivason, On generalizing the two-poisson model, Journal of the American Society for Information Science 41(1) (1989), 61-66.
46. C. Tullo and J. Hurford, Modelling zipfian distributions in language. In Proceedings of the ESSLLI Workshop on Language Evolution and Computation, Vienna, 2003.
47. P. Turney, Learning algorithms for keyphrase extraction, Information Retrieval 2(4) (2000), 303-336.
48. C. van Rijsbergen, Automatic text analysis, in: Information Retrieval University of Glasgow, 1979.
49. P. Vitanyi, Universal similarity. In Proceedings of the IEEE ITSOC Information Theory Workshop on Coding and Complexity, New Zealand, 2005.
50. E. Voorhees and D. Harman, Trec Experiment and Evaluation in Information Retrieval, MIT Press, MA, USA, 2005.
51. W. Wong, Practical approach to knowledge-based question answering with natural language understanding and advanced reasoning. Master's thesis, National Technical University College of Malaysia, arXiv:cS.CL/ 0707.3559, 2005.
52. W. Wong, W. Liu and M. Bennamoun, Determining termhood for learning domain ontologies using domain prevalence and tendency. In Proceedings of the 6th Australasian Conference on Data Mining (AusDM) Gold Coast, 2007.
53. W. Wong, W. Liu and M. Bennamoun, Determining the unithood of word sequences using mutual information and independence measure. In Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics (PACLING), Melbourne, Australia, 2007.
54. W. Wong, W. Liu and M. Bennamoun, Tree-traversing ant algorithm for term clustering based on featureless similarities, Data Mining and Knowledge Discovery 15(3) (2007), 349-381.
55. W. Wong, W. Liu and M. Bennamoun, Determination of unithood and termhood for term extraction, in: Handbook of Research on Text and Web Mining Technologies, M. Song and Y. Wu, eds, IGI Global, 2008.
56. G. Zipf, The Psycho-Biology of Language, Houghton Mifflin, Boston, MA, 1935.
57. G. Zipf, Human Behaviour and the Principle of Least-Effort, Addison-Wesley, Cambridge, MA, 1949.