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Using Grammatical Inference to Improve Precision in Information Extraction
 In ICML97 Workshop on Automation Induction, Grammatical Inference, and Language Acquisition
, 1997
"... The field of information extraction (IE) is concerned with applying natural language processing (NLP) and information retrieval (IR) techniques to the automatic extraction of essential details from text documents. We are exploring the use of machine learning methods for IE. While the most promising ..."
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Cited by 27 (5 self)
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The field of information extraction (IE) is concerned with applying natural language processing (NLP) and information retrieval (IR) techniques to the automatic extraction of essential details from text documents. We are exploring the use of machine learning methods for IE. While the most promising methods we have developed perform well for problems defined over a collection of electronic seminar announcements, they are imprecise in their identification of the boundaries of relevant text fragments (fields). Here, we entertain the idea of using grammatical inference (GI) methods to learn the appropriate form of a field. We describe one method for translating raw text into an abstract alphabet suitable for GI, and show that, by combining one IE learning method with the resulting inferred grammars, large improvements in precision can be realized for some fields. Introduction The recent explosion in the availability and accessibility of online text documents has given strong impetus to d...
Probabilistic DFA Inference using KullbackLeibler Divergence and Minimality
 In Seventeenth International Conference on Machine Learning
, 2000
"... Probabilistic DFA inference is the problem of inducing a stochastic regular grammar from a positive sample of an unknown language. The ALERGIA algorithm is one of the most successful approaches to this problem. In the present work we review this algorithm and explain why its generalization criterion ..."
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Cited by 11 (2 self)
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Probabilistic DFA inference is the problem of inducing a stochastic regular grammar from a positive sample of an unknown language. The ALERGIA algorithm is one of the most successful approaches to this problem. In the present work we review this algorithm and explain why its generalization criterion, a state merging operation, is purely local. This characteristic leads to the conclusion that there is no explicit way to bound the divergence between the distribution de ned by the solution and the training set distribution (that is, to control globally the generalization from the training sample). In this paper we present an alternative approach, the MDI algorithm, in which the solution is a probabilistic automaton that trades o minimal divergence from the training sample and minimal size. An e cient computation of the KullbackLeibler divergence between two probabilistic DFAs is described, from which the new learning criterion is derived. Empirical results in the d...
Using Symbol Clustering to Improve Probabilistic Automaton Inference
, 1998
"... . In this paper we show that clustering alphabet symbols before PDFA inference is performed reduces perplexity on new data. This result is especially important in real tasks, such as spoken language interfaces, in which data sparseness is a significant issue. We describe the application of the ALERG ..."
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Cited by 9 (2 self)
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. In this paper we show that clustering alphabet symbols before PDFA inference is performed reduces perplexity on new data. This result is especially important in real tasks, such as spoken language interfaces, in which data sparseness is a significant issue. We describe the application of the ALERGIA algorithm combined with an independent clustering technique to the Air Travel Information System (ATIS) task. A 25 % reduction in perplexity was obtained. This result outperforms a trigram model under the same simple smoothing scheme. 1 Introduction Inference of deterministic finite automaton (DFA) from positive and negative data can be solved by the RPNI algorithm, proposed independently by Trakhtenbrot et al. [16] and by Oncina et al. [13]. This algorithm was used by Lang in his extensive experimental study of learning random deterministic automata from sparse samples [10]. An adapted version of this algorithm proved to be successful in the recent Abbadingo competition [9]. However th...
Learning Hidden Markov Models to Fit LongTerm Dependencies
, 2005
"... this report a novel approach to the induction of the structure of Hidden Markov Models (HMMs). The notion of partially observable Markov models (POMMs) is introduced. POMMs form a particular case of HMMs where any state emits a single letter with probability one, but several states can emit the ..."
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Cited by 2 (2 self)
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this report a novel approach to the induction of the structure of Hidden Markov Models (HMMs). The notion of partially observable Markov models (POMMs) is introduced. POMMs form a particular case of HMMs where any state emits a single letter with probability one, but several states can emit the same letter. It is shown that any HMM can be represented by an equivalent POMM. The proposed induction algorithm aims at finding a POMM fitting the dynamics of the target machine, that is to best approximate the stationary distribution and the mean first passage times observed in the sample. The induction relies on nonlinear optimization and iterative state splitting from an initial order one Markov chain. Experimental results illustrate the advantages of the proposed approach as compared to BaumWelch HMM estimation or backo# smoothed Ngrams equivalent to variable order Markov chains
Classification of Banded Chromosomes using ErrorCorrecting Grammatical Inference (ECGI) and Multilayer Perceptron (MLP)
, 1997
"... The application of two generalpurpose Pattern Recognition techniques (a Syntactic approach, Error Correcting Grammatical Inference, and a Geometric approach, Multilayer Percptron) to unidimensionally coded human chromosome recognition is considered in this paper. The results obtained with both t ..."
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The application of two generalpurpose Pattern Recognition techniques (a Syntactic approach, Error Correcting Grammatical Inference, and a Geometric approach, Multilayer Percptron) to unidimensionally coded human chromosome recognition is considered in this paper. The results obtained with both techniques are comparable or better than previous results obtained by other authors with the very same data using methods specifically designed for this application.