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Inference of Stochastic Regular Grammars by Massively Parallel Genetic Algorithms
 In Proceedings of the Sixth International Conference on Genetic Algorithms
, 1995
"... A genetic approach to the inference of stochastic regular grammars from a given finite set of sample words is presented. The goal of the inference problem is not only to find a grammar that covers the given finite sample, but possibly also the infinite language from which the sample was taken (gener ..."
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Cited by 10 (2 self)
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A genetic approach to the inference of stochastic regular grammars from a given finite set of sample words is presented. The goal of the inference problem is not only to find a grammar that covers the given finite sample, but possibly also the infinite language from which the sample was taken (generalization). We propose two different bitstring representation methods for stochastic regular grammars and have a closer look at the objective function. Due to the large complexity of the problem, a massively parallel implementation of genetic algorithms was used. The algorithm was applied to a workloadmodelingproblem. The results are compared with reference methods like the successor, ktail and kTLSSmethod. 1 INTRODUCTION Genetic algorithms have successfully been used as a powerful global optimization method for problems with a large search space and a multimodal or otherwise difficult objective function. One such problem is the construction of a grammar for a (finite or infinite) lan...
Statistical Source Channel Models for Natural Language Understanding
, 1996
"... d my ignorance in the field. He was always patient, and took the time to explain his answers at a level I could understand. iv Dr. Todd Ward, a colleague of mine at IBM, has also "been there" for me. I cannot count the number of times that Todd helped me figure out a solution to a problem, either ..."
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Cited by 10 (1 self)
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d my ignorance in the field. He was always patient, and took the time to explain his answers at a level I could understand. iv Dr. Todd Ward, a colleague of mine at IBM, has also "been there" for me. I cannot count the number of times that Todd helped me figure out a solution to a problem, either mathematical or programming. Whenever I was not sure about a solution to a problem, Todd was my sounding board. I'm sure that his individual research efforts were slowed by our meetings, but that never stopped him from helping me. Todd also acted as a counselor, providing insight on how to complete a doctorate! Former IBMer, Dr. Stephen Della Pietra, is without a doubt the brightest mathematician with whom I have ever worked. Like Salim and Todd, he knows statistical modeling at a much greater depth than I do, and he never minded "bringing down" the level of his explanations to one where I could understand and absorb the material. Stephen was my mentor, and without his expert tutelag
Grammar Learning for Spoken Language Understanding
 In Proceedings of ASRU Workshop. Madonna di Campigilo
, 2001
"... Many stateoftheart conversational systems use semanticbased robust understanding and manually derived grammars, a very timeconsuming and errorprone process. This paper describes a machineaided grammar authoring system that enables a programmer to rapidly develop a high quality grammar for con ..."
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Cited by 10 (4 self)
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Many stateoftheart conversational systems use semanticbased robust understanding and manually derived grammars, a very timeconsuming and errorprone process. This paper describes a machineaided grammar authoring system that enables a programmer to rapidly develop a high quality grammar for conversational systems. This is achieved with a combination of domainspecific semantics, a library grammar, syntactic constraints and a small amount of example sentences that have been semantically annotated. Our experiments show that the learned semantic grammars consistently outperform manually authored grammars requiring much less authoring load. 1.
Probabilistic Pattern Matching and the Evolution of Stochastic Regular Expressions
 International Journal of Applied Intelligence
, 1999
"... The use of genetic programming for probabilistic pattern matching is investigated. A stochastic regular expression language is used. The language features a statistically sound semantics, as well as a syntax that promotes efficient manipulation by genetic programming operators. An algorithm for effi ..."
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Cited by 9 (5 self)
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The use of genetic programming for probabilistic pattern matching is investigated. A stochastic regular expression language is used. The language features a statistically sound semantics, as well as a syntax that promotes efficient manipulation by genetic programming operators. An algorithm for efficient string recognition based on approaches in conventional regular language recognition is used. When attempting to recognize a particular test string, the recognition algorithm computes the probabilities of generating that string and all its prefixes with the given stochastic regular expression. To promote efficiency, intermediate computed probabilities that exceed a given cutoff value will preempt particular interpretation paths, and hence prune unconstructive interpretation. A few experiments in recognizing stochastic regular languages are discussed. Application of the technology in bioinformatics is in progress.
A Polynomial Time Incremental Algorithm for Learning DFA
"... We present an efficient incremental algorithm for learning deterministic finite state automata (DFA) from labeled examples and membership queries. This algorithm is an extension of Angluin's ID procedure to an incremental framework. The learning algorithm is intermittently provided with labeled ..."
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Cited by 9 (4 self)
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We present an efficient incremental algorithm for learning deterministic finite state automata (DFA) from labeled examples and membership queries. This algorithm is an extension of Angluin's ID procedure to an incremental framework. The learning algorithm is intermittently provided with labeled examples and has access to a knowledgeable teacher capable of answering membership queries. The learner constructs an initial hypothesis from the given set of labeled examples and the teacher's responses to membership queries. If an additional example observed by the learner is inconsistent with the current hypothesis then the hypothesis is modified minimally to make it consistent with the new example. The update procedure ensures that the modified hypothesis is consistent with all examples observed thus far. The algorithm is guaranteed to converge to a minimum state DFA corresponding to the target when the set of examples observed by the learner includes a live complete set. We prove the convergence of this algorithm and analyze its time and space complexities.
Natural Language Grammatical Inference
, 1995
"... This project is concerned with programming a computer to make predictions about which words are most likely to follow a small segment of English text. At first this may seem a strange problem, but I intend to show that there exist a wide range of applications that would benefit from such a program. ..."
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Cited by 8 (2 self)
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This project is concerned with programming a computer to make predictions about which words are most likely to follow a small segment of English text. At first this may seem a strange problem, but I intend to show that there exist a wide range of applications that would benefit from such a program. Indeed, my motivation for approaching this problem was to provide a way of improving the accuracy of speech recognition systems. Additionally, I am interested with the problem of Grammatical Inference. In fact, the word prediction problem and the Grammatical Inference problem are intertwined, and it seems that approaching either one will lead to the other. Grammatical Inference entails inferring a grammar for an arbitrary language from a finite set of sample sentences in the language. It is quite easy to measure the performance of a word prediction system, providing that its prediction is given as a probability distribution. This allows us to compare our predictor with others, such as the tr...
Analyzing And Improving Statistical Language Models For Speech Recognition
, 1994
"... A speech recognizer is a device that translates speech into text. Many current speech recognizers contain two components, an acoustic model and a statistical language model. The acoustic model indicates how likely it is that a certain word corresponds to a part of the acoustic signal (e.g. the speec ..."
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Cited by 5 (0 self)
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A speech recognizer is a device that translates speech into text. Many current speech recognizers contain two components, an acoustic model and a statistical language model. The acoustic model indicates how likely it is that a certain word corresponds to a part of the acoustic signal (e.g. the speech). The statistical language model indicates how likely it is that a certain word will be spoken next, given the words recognized so far. Even though the acoustic model might for example not be able to decide between the acoustically similar words "peach" and "teach", the statistical language model can indicate that the word "peach" is more likely if the previously recognized words are "He ate the". Current speech recognizers perform well on constrained tasks, but the goal of continuous, speaker independent speech recognition in potentially noisy environments with a very large vocabulary has not been reached so far. How can statistical language models be improved so that more complex tasks c...
String Pattern Matching For A Deluge Survival Kit
, 2000
"... String Pattern Matching concerns itself with algorithmic and combinatorial issues related to matching and searching on linearly arranged sequences of symbols, arguably the simplest possible discrete structures. As unprecedented volumes of sequence data are amassed, disseminated and shared at an incr ..."
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Cited by 5 (1 self)
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String Pattern Matching concerns itself with algorithmic and combinatorial issues related to matching and searching on linearly arranged sequences of symbols, arguably the simplest possible discrete structures. As unprecedented volumes of sequence data are amassed, disseminated and shared at an increasing pace, effective access to, and manipulation of such data depend crucially on the efficiency with which strings are structured, compressed, transmitted, stored, searched and retrieved. This paper samples from this perspective, and with the authors' own bias, a rich arsenal of ideas and techniques developed in more than three decades of history.
Rapid Development of Spoken Language Understanding Grammars
"... To facilitate the development of spoken dialog systems and speech enabled applications, we introduce SGStudio (Semantic Grammar Studio), a grammar authoring tool that enables regular software developers with little speech/linguistic background to rapidly create quality semantic grammars for automati ..."
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Cited by 5 (0 self)
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To facilitate the development of spoken dialog systems and speech enabled applications, we introduce SGStudio (Semantic Grammar Studio), a grammar authoring tool that enables regular software developers with little speech/linguistic background to rapidly create quality semantic grammars for automatic speech recognition (ASR) and spoken language understanding (SLU). We focus on the underlying technology of SGStudio, including knowledge assisted examplebased grammar learning, grammar controls and configurable grammar structures. While the focus of SGStudio is to increase productivity, experimental results show that it also improves the quality of the grammars being developed. Key words: Automatic grammar generation, context free grammars (CFGs), examplebased grammar learning, grammar controls, hidden Markov models (HMMs), ngram model, automatic speech recognition (ASR), spoken language understanding
Approximate identification of automata
 Electronics Letters
, 1975
"... A technique is described for the identification of probabilistic and other nondeterministic automata from sequences of their input/output behaviour. For a given number of states the models obtained are optimal in well defined senses, one related to leastmeansquare approximation and the other to S ..."
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Cited by 3 (3 self)
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A technique is described for the identification of probabilistic and other nondeterministic automata from sequences of their input/output behaviour. For a given number of states the models obtained are optimal in well defined senses, one related to leastmeansquare approximation and the other to Shannon entropy. Practical and theoretical investigations of the technique are outlined. 1