Coupling as Strategy for Reducing Concept-Drift in Never-ending Learning Environments

• Autorzy: Hruschka Jr E. R. , Duarte M. C. , Nicoletti M. C.
• Języki publikacji: EN

Abstrakty

EN

The project and implementation of autonomous computational systems that incrementally learn and use what has been learnt to, continually, refine its learning abilities throughout time is still a goal far from being achieved. Such dynamic systems would conform to the main ideas of the automatic learning model conventionally characterized as never-ending learning (NEL). The never-ending approach to learning exhibits similarities to the semi-supervised (SS) model which has been successfully implemented by bootstrap learning methods. Bootstrap learning has been one of the most successful among the SS-methods proposed to date and, as such, the natural candidate for implementing NEL systems. Bootstrap methods learn from an available labeled set of data, use the induced knowledge to label some unlabeled new data and, recurrently, learn again from both sets of data in a cyclic manner. However the use of SS methods, particularly bootstrapping methods, to implement NEL systems can give rise to a problem known as concept-drift. Errors that may occur when the system automatically labels new unlabeled data can, over time, cause the system to run off track. The development of new strategies to lessen the impact of concept-drift is an important issue that should be addressed if the goal is to increase the plausibility of developing such systems, employing bootstrap methods. Coupling techniques can play an important role in reducing concept-drift effects over machine learning systems, particularly those designed to perform tasks related to machine reading. This paper proposes and formalizes relevant coupling strategies for dealing with the concept-drift problem in a NEL environment implemented as the system RTWP (Read The Web in Portuguese); initial results have shown they are promising strategies for minimizing the problem taking into account a few system settings.

Słowa kluczowe

EN

autonomous intelligent system; bootstrap method; concept drift; coupling; machine learning; machine reading; never-ending learning; semi-supervised learning

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 124, nr 1/2
• Strony: 47--61
• Opis fizyczny: Bibliogr. 29 poz., wykr.

Twórcy

autor

Hruschka Jr E. R.

• Department of Computer Science, UFSCar, S. Carlos, SP, Brazil

autor

Duarte M. C.

• Department of Computer Science, UFSCar, S. Carlos, SP, Brazil

autor

Nicoletti M. C.

• Department of Computer Science, UFSCar, S. Carlos, SP, Brazil

Bibliografia

• [1] Banko, M., Cafarella, M. J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the Web, in: Proc. of The International Joint Conference on Artificial Intelligence, California: Morgan Kaufmann Publishers Inc., 2007, 2670-2676.
• [2] Banko, M., Etzioni, O.: The tradeoffs between open and traditional relation extraction, in: Proceedings of The Annual Meeting of the Association for Computational Linguistics, 2008, 28-36.
• [3] Betteridge, J., Carlson, A., Hong, S. A., Hruschka Jr., E. R., Law, E. L. M., Mitchell, T. M., Wang, S. H.: Toward never ending language learning, in: Proceedings of The AAAI 2009 Spring Symposium on Learning by Reading and Learning to Read, Palo Alto, 2009, 1-2.
• [4] Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training, in: Proceedings of The Annual Conference on Computational Learning Theory (COLT), Madison, USA: ACM, 1998, 92-100.
• [5] Carlson, A., Betteridge, J., Hruschka Jr., E. R., Mitchell, T. M.: Coupling semi-supervised learning of categories and relations, in: Proceedings of The NAACL HLT Workshop on Semi-supervised Learning for Natural Language Processing, Colorado, USA, 2009, 1-9.
• [6] Carlson, A., Betteridge, J., Wang, R. C., Hruschka Jr., E. R., Mitchell, T. M.: Coupled semi-supervised learning for information extraction, in: Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), ACM, New York, NY, USA, 2010, 101-110.
• [7] Carlson, A.: Coupled semi-supervised learning, PhD. Thesis School of Computer Science, Carnegie Mellon University, Pittsburgh, USA, 2010.
• [8] Carlson, A., Betteridge, J., Kistel, B., Settles, B., Hruschka Jr., E. R., Mitchell, T. M.: Toward an architecture for never-ending language learning, in: Proceedings of the Conference on Artificial Intelligence (AAAI), 2010, 1306-1313.
• [9] Chang, M.-W., Ratinov, L.-A., Roth, D.: Guiding semi-supervision with constraint-driven learning, in:Proceedings of the 45th Annual Meeting of the Association of Computational Linguistic, 2007, 280-287.
• [10] Chapelle, O., Scholkopf, B., Zien, A.: Semi-Supervised Learning, Cambridge, MA: MIT Press, 2006.
• [11] Culp, M., Michailidis, G.: An iterative algorithm for extending learners to a semi-supervised setting, Journal of Computational and Graphical Statistics, 17(3), 2008, 545-571.
• [12] Curran, J.R., Murphy, T., Scholz, B.: Minimising semantic drift with mutual exclusion bootstrapping, in: Proceedings of The 10th Conference of the Pacific Association for Computational Linguistics (PACLING 07), 2007, 172-180.
• [13] Duarte, M. C., Hruschka Jr., E. R., Nicoletti, M. C.: Minimizing the impact of the concept-drift problem by means of coupling in a never-ending learning environment, (in Portuguese), in: Proceedings of the 8th Brazilian Symposium in Information and Human Language Technology, 2011, 134-143.
• [14] Etizioni, O., Banko, M., Cafarella, M. J.: Machine reading, in: Proceedings of The 21st National Conference on Artificial Intelligence (AAAI), 2006, 1517-1519.
• [15] Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011, 1535-1545.
• [16] Haffari, G., Sarkar, A.: Analysis of semi-supervised learning with the Yarowsky algorithm, in: Proceedings of The 23rd Conference on Uncertainty in Artificial Intelligence (UAI), 2007, 159-166.
• [17] Hearst, M. A.: Automatic acquisition of hyponyms from large text corpora. in: Proceedings of the 14th conference on Computational linguistics - (COLING ’92), vol. 2, Association for Computational Linguistics, 1992, 539-545.
• [18] Hoffart, J., Suchanek, F. M., Berberich, K., Lewis-Kelham, E., Melo, G., Weikum, G.: YAGO2: exploring and querying world knowledge in time, space, context, and many languages. in: Proceedings of the 20th International Conference Companion on World Wide Web (WWW ’11), 2011, 229-232.
• [19] McClosky, D., Charniak, E., Johnson, M.: Effective self-training for parsing, in: Proceedings of The Human Language Technology Conference of the NAACL, New York, USA, 2006, 152-159.
• [20] Mitchell, T. M., Allen, J., Chalasani, P., Cheng, J., Etzioni, O., Ringuette, M. N., Schlimmer, J. C.: A framework for self-improving systems, Arch. for Intelligence, 1991, 323-356.
• [21] Mitchell, T. M., Betteridge, J., Carlson, A., Hruschka, E., Wang, R.: Populating the semantic web by macroreading internet text, in: Proceedings of International Semantic Web Conference, Chantilly: Springer- Verlag, 2009, 998-1002.
• [22] Mohamed, T., Hruschka Jr., E.R., Mitchell, T. M.: Discovering relations between noun categories. in: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2011, 14471455.
• [23] Quinlan, J. R., Cameron-Jones, R. M: Foil: a midterm report, in: Proceedings of The 6th European Conference on Machine Learning, (ECML 93), LNAI, vol. 667, 1993, 3-20.
• [24] Riloff, E., Jones, R.: Learning dictionaries for information extraction by multi-level bootstrapping, in: Proceedings of The Sixteenth National Conference on Artificial Intelligence (AAAI 1999), 2009, 474-479.
• [25] Rosenberg, C., Hebert, M., Schneiderman, H.: Semi-supervised self-training of object detection models, in: Proceedings of The Seventh IEEE Workshop on Applications of Computer Vision, 2005.
• [26] Rosenfeld, B., Feldman, R.: Using corpus statistics on entities to improve semi-supervised relation extraction from the Web, in: Proceedings of The 45th Annual Meeting of the Association for Computational Linguistics (ACL 2007), 2007, 600-607.
• [27] Suchanek, F. M., Kasneci, G., Weikum, G.: YAGO: A Large Ontology from Wikipedia and WordNet. Journal Web Semantic, 6(3), 2008, 203-217.
• [28] Yangarber, R.: Counter-training in discovery of semantic patterns, in: Proceedings of the 41st Annual Meeting of Association of Computational Linguistics (ACL 03), 2003, 343-350.
• [29] Zhu, X.: Semi-supervised learning literature survey, Computer Sciences Technical Report 1530, University of Wisconsin-Madison, 2008.