Abstract
The problem of extracting knowledge from large volumes of unstructured textual information has become increasingly important. We consider the problem of extracting text slices that adhere to a syntactic pattern and propose an approach capable of generating the desired pattern automatically, from a few annotated examples. Our approach is based on Genetic Programming and generates extraction patterns in the form of regular expressions that may be input to existing engines without any post-processing. Key feature of our proposal is its ability of discovering automatically whether the extraction task may be solved by a single pattern, or rather a set of multiple patterns is required. We obtain this property by means of a separate-and-conquer strategy: once a candidate pattern provides adequate performance on a subset of the examples, the pattern is inserted into the set of final solutions and the evolutionary search continues on a smaller set of examples including only those not yet solved adequately. Our proposal outperforms an earlier state-of-the-art approach on three challenging datasets.
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Barrero, D.F., R-Moreno, M.D., Camacho, D.: Adapting searchy to extract data using evolved wrappers. Expert Syst. Appl. 39(3), 3061–3070 (2012). http://www.sciencedirect.com/science/article/pii/S0957417411012991
Barrero, D.F., Camacho, D., R-Moreno, M.D.: Automatic web data extraction based on genetic algorithms and regular expressions. In: Cao, L. (ed.) Data Mining and Multi-agent Integration, pp. 143–154. Springer, Heidelberg (2009). http://www.springerlink.com/index/G1K1N12060742860.pdf
Barros, R.C., Basgalupp, M.P., de Carvalho, A.C., Freitas, A.A.: A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, pp. 1237–1244. ACM (2012)
Bartoli, A., Davanzo, G., De Lorenzo, A., Medvet, E., Sorio, E.: Automatic synthesis of regular expressions from examples. Computer 47(12), 72–80 (2014)
Bartoli, A., De Lorenzo, A., Medvet, E., Tarlao, F.: Playing regex golf with genetic programming. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, pp. 1063–1070. ACM (2014)
Brauer, F., Rieger, R., Mocan, A., Barczynski, W.: Enabling information extraction by inference of regular expressions from sample entities. In: ACM International Conference on Information and Knowledge Management, pp. 1285–1294. ACM (2011). http://dl.acm.org/citation.cfm?id=2063763
Cetinkaya, A.: Regular expression generation through grammatical evolution. In: International Conference on Genetic and Evolutionary Computation, GECCO, pp. 2643–2646. ACM, New York, NY, USA (2007). http://doi.acm.org/10.1145/1274000.1274089
De Lorenzo, A., Medvet, E., Bartoli, A.: Automatic string replace by examples. In: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation, pp. 1253–1260. ACM (2013)
Eggermont, J., Kok, J.N., Kosters, W.A.: Genetic programming for data classification: partitioning the search space. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 1001–1005. ACM (2004)
Fürnkranz, J.: Separate-and-conquer rule learning. Artif. Intell. Rev. 13(1), 3–54 (1999)
Gulwani, S.: Automating string processing in spreadsheets using input-output examples. In: Proceedings of the 38th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, POPL 2011, pp. 317–330. ACM, New York, NY, USA (2011). http://doi.acm.org/10.1145/1926385.1926423
Kinber, E.: Learning regular expressions from representative examples and membership queries. Grammatical Inference: Theoretical Results and Applications, pp. 94–108 (2010). http://www.springerlink.com/index/4T83103160M9PQ74.pdf
Lang, K.J., Pearlmutter, B.A., Price, R.A.: Results of the Abbadingo one DFA learning competition and a new evidence-driven state merging algorithm. In: Honavar, V., Slutzki, G., Slutzki, G. (eds.) Grammatical Inference. LNCS, vol. 1433, pp. 1–12. Springer, Heidelberg (1998). http://link.springer.com/chapter/10.1007/BFb0054059
Le, V., Gulwani, S.: Flashextract: A framework for data extraction by examples. In: Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation, p. 55. ACM (2014)
Li, Y., Krishnamurthy, R., Raghavan, S., Vaithyanathan, S., Arbor, A.: Regular Expression Learning for Information Extraction. Computational Linguistics, pp. 21–30 (October, 2008). http://portal.acm.org/citation.cfm?doid=1613715.1613719
Lucas, S.M., Reynolds, T.J.: Learning deterministic finite automata with a smart state labeling evolutionary algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 27(7), 1063–1074 (2005). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1432740
Menon, A., Tamuz, O., Gulwani, S., Lampson, B., Kalai, A.: A machine learning framework for programming by example. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 187–95 (2013). http://machinelearning.wustl.edu/mlpapers/papers/ICML2013_menon13
Pappa, G.L., Freitas, A.A.: Evolving rule induction algorithms with multi-objective grammar-based genetic programming. Knowl. Inf. Syst. 19(3), 283–309 (2009)
Wu, T., Pottenger, W.: A semi-supervised active learning algorithm for information extraction from textual data. J. Am. Soc. Inf. Sci. Technol. 56(3), 258–271 (2005)
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Bartoli, A., De Lorenzo, A., Medvet, E., Tarlao, F. (2015). Learning Text Patterns Using Separate-and-Conquer Genetic Programming. In: Machado, P., et al. Genetic Programming. EuroGP 2015. Lecture Notes in Computer Science(), vol 9025. Springer, Cham. https://doi.org/10.1007/978-3-319-16501-1_2
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