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# IEE Colloquium on Grammatical Inference: Theory, Applications and Alternatives

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Displaying Results 1 - 25 of 33
• ### New directions in grammatical inference

Publication Year: 1993, Page(s):1/1 - 1/7
| | PDF (2045 KB)

An introduction is given to grammatical inference, theory, application and alternatives. Grammatical interference is an important field of research, and it has suffered from the lack of a focused research community. The author discusses what sort of grammars there are and what they can do, then provides a discussion of the types of grammatical inference algorithm that have been successfully employ... View full abstract»

• ### Context free grammar induction using genetic algorithms

Publication Year: 1993, Page(s):P11/1 - P11/5
| | PDF (388 KB)

A genetic algorithm was developed for the purpose of inferring context free grammars. Results are reported on the inference of two grammars in this class. Various forms of the grammar to generate the language of correctly balanced and nested brackets were successfully inferred, but more complex grammars were not learnt with the resources available. The author also discusses various issues such as ... View full abstract»

• ### Integrating segmentation and recognition in on-line cursive handwriting using error-correcting grammars

Publication Year: 1993, Page(s):23/1 - 23/6
| | PDF (388 KB)

A grammar-based approach is given to recognizing online cursive handwriting that overcomes the inherent problems of variability in input size and the need to integrate segmentation and recognition. The input stream is represented as a sequence of uniform stroke descriptions that are processed by a mixture of neural-networks, each designed to recognize letters of different sizes. Words are then rec... View full abstract»

• ### Algebraic grammatical inference

Publication Year: 1993, Page(s):P12/1 - P1210
Cited by:  Papers (1)
| | PDF (656 KB)

A description is given of an approach to grammatical inference. Given both a positive and negative sample of the language to be learned, each string is mapped into a multi-variate polynomial expression. These expressions are each given values depending on their membership of the language. If we are inferring a non-stochastic grammar, then each expression derived from a positive string must equate ... View full abstract»

• ### Application of the error-correcting grammatical inference algorithm (ECGI) to planar shape recognition

Publication Year: 1993, Page(s):24/1 - 2410
Cited by:  Papers (3)
| | PDF (748 KB)

ECGI is an error-correcting-based learning technique that aims at obtaining structural finite-state models of (unidimensional) objects from samples of these objects. The learning procedure captures certain useful regularities of the training data in the object-models, while also obtaining appropriate models of the irregularities' (errors and distortions) that these data tend to exhibit with respe... View full abstract»

• ### Automatically acquiring and evaluating a classification of words

Publication Year: 1993, Page(s):P8/1 - P8/7
| | PDF (356 KB)

An investigation is made into how to automatically derive a meaningful classification of words from ordinary text on the assumption that words of similar role usually appear in similar contexts. The authors propose novel patterns in text which derive good contextual information. They describe methods by which the contextual information can be used to classify like words together. Methods to descri... View full abstract»

• ### Learning, extracting, inserting and verifying grammatical information in recurrent neural networks

Publication Year: 1993
Cited by:  Patents (1)
| | PDF (96 KB)

Recurrent neural networks can be trained from string examples to behave like deterministic finite-state automata (DFA's) and pushdown automata (adapts) i.e. they recognize respectively deterministic regular and context-free grammars (DCFG's). The author discusses some of the successes and failures of this type of recurrent neural network' grammatical inference engine, as well as some of the issue... View full abstract»

• ### Adaptive statistical and grammar models of language for application to speech recognition

Publication Year: 1993, Page(s):25/1 - 25/8
Cited by:  Patents (1)
| | PDF (372 KB)

The statistical and syntactic approaches to the modelling of language are consolidated in order to improve performance in speech recognition. The authors also aim to minimise the need for human intervention in the training of the language model from a corpus. Hybrid speech recognition systems using both bigram and grammar models can yield improved performance compared with the use of either model ... View full abstract»

• ### Deriving a probabilistic grammar of semantic markers from unrestricted English text

Publication Year: 1993, Page(s):P9/1 - P9/7
| | PDF (368 KB)

The derivation is described of a probabilistic grammar for main subject field codes from the machine readable version of the Longman Dictionary of Contemporary English (LDOCE) (P. Procter, 1978). These codes are used in the dictionary to mark the subject area to which a certain sense of a word belongs. The grammar consists of the dictionary itself and a matrix that describes how closely two main s... View full abstract»

• ### Learning context-free grammar with enhanced neural network pushdown automaton

Publication Year: 1993, Page(s):P6/1 - P613
Cited by:  Papers (1)
| | PDF (904 KB)

Previously (C. Sreerupa Das et al., 1992, 1993; C.L. Giles et al., 1990; G.Z. Sun et al., 1990, 1991), a model was developed of neural network pushdown automata (NNPDA). NNPDA is a hybrid system which couples the neural network finite controller with an external continuous stack memory. It learns context-free grammars from examples by minimizing the properly defined objective function. In the orig... View full abstract»

• ### Probabilistic dependency grammar, and its application in constructing language models for speech applications

Publication Year: 1993, Page(s):12/1 - 1211
| | PDF (748 KB)

Dependency grammar defines grammatical notions in terms of direct links between words at a lexical level. These lexical dependency relations are considered more fundamental than other representations, such as phrase structure analyses, which can be derived from them. In simple cases there is a direct correspondence between dependencies and phrase structures. The dependencies for a given word can b... View full abstract»

• ### Regular inference with maximal valid grammar method

Publication Year: 1993, Page(s):5/1 - 5/9
| | PDF (496 KB)

The aim of grammatical inference is defined from a new point of view. The maximal valid grammar method is proposed to infer context free grammars from structural positive samples using negative samples. A polynomial algorithm is given. The MVG method is applied on the class of regular grammars View full abstract»

• ### Rule-based knowledge in neural computing

Publication Year: 1993, Page(s):19/1 - 19/8
Cited by:  Patents (1)
| | PDF (432 KB)

Similar to humans, an information processing system should be able to exploit knowledge that is presented in form of rules as well as information that is acquired through experience. The author demonstrates how rule-based knowledge can be used to pre-structure a neural network. In this way, the network has problem specific knowledge prior to training. After training, the altered rules can be extra... View full abstract»

• ### Inferring grammar from lexis: machine-readable dictionaries as sources of wholesale syntactic and semantic information

Publication Year: 1993, Page(s):P3/1 - P3/5
Cited by:  Papers (1)  |  Patents (6)
| | PDF (276 KB)

The main idea behind research into machine-readable dictionaries (MRDs) is that the re-use of such resources enables the NLP community to build up large-scale lexicons with a realistic level of coverage instead of the customary purpose-built `toy' lexicons containing just a few sample items. Most of the earlier work in this area was concerned with the systematic extraction of easily identifiable t... View full abstract»

• ### Inference of stochastic regular languages through simple recurrent networks

Publication Year: 1993, Page(s):16/1 - 16/6
| | PDF (392 KB)

Grammatical inference has been recently approached through artificial neural networks. Recurrent connectionist architectures were trained to accept or reject strings belonging to a number of specific regular languages, or to predict the possible successor(s) for each character in the string. On the other hand, for static (non-string) data, M.D. Richard et al. (1991), showed that a nonrecurrent arc... View full abstract»

• ### Parse tree n-grams for spoken language modelling

Publication Year: 1993, Page(s):26/1 - 26/6
Cited by:  Patents (2)
| | PDF (300 KB)

A method is described for modelling natural language for speech recognition. Its aim is to incorporate the advantages of two previous types of approach; the statistical approach and the formal linguistic approach. The n-gram model (J.K. Baker, 1975), is a statistical model based on corpus data, and is used to estimate the probability of an unseen sequence of words based on this data. A phrase stru... View full abstract»

• ### Grammatical inference based on hyperedge replacement: a summary

Publication Year: 1993, Page(s):7/1 - 7/6
Cited by:  Papers (1)
| | PDF (388 KB)

As a fundamental goal of pattern generation and recognition, one wants to find syntactic descriptions for certain types or sets of patterns in such a way that automatic generation or recognition of the patterns is provided. The types or sets of patterns, that are of interest, may not be known completely, but only by some samples. A grammatical-inference algorithm is introduced which constructs hyp... View full abstract»

• ### Analysis of a simple bipos language model-attempt at a strategy to improve language models for speech recognition

Publication Year: 1993, Page(s):P10/1 - P10/8
| | PDF (540 KB)

A speech recognizer has to choose, at each point in the utterance, the words among all the words in the vocabulary, that are the most likely. To that end, it uses an acoustic model and a language model and the author focuses on the language model. The bipos model is presented and analysed. A method is introduced called probability decomposition to measure which part of the model is performing part... View full abstract»

• ### The application of k-testable languages in the strict sense to phone recognition in automatic speech recognition

Publication Year: 1993, Page(s):22/1 - 22/7
Cited by:  Papers (1)
| | PDF (564 KB)

The aim of the work is to show the feasibility and the appeal of characterizable methods of grammatical inference in establishing the required structural models for particular syntactic pattern recognition problems, so as to allow a satisfactory performance to be achieved. In particular, the authors have chosen the application area of automatic speech recognition. They describe two applications in... View full abstract»

• ### A syntax based grammar of stress sequences

Publication Year: 1993, Page(s):P7/1 - P7/7
Cited by:  Patents (1)
| | PDF (404 KB)

The Spoken English Corpus (SEC) is a speech corpus containing prosodic and syntactic annotations and provides an ideal source for the study of the relationship between prosody and syntax. It is shown that the placement of stresses on words in an utterance may be largely predicted from word classifications. The language model used to make these predictions is inferred from the SEC training set. How... View full abstract»

• ### Inductive and deductive learning of grammar: dealing with incomplete theories

Publication Year: 1993, Page(s):13/1 - 1310
| | PDF (424 KB)

A framework is given for learning plausible unification-based natural language grammars. The authors assume that the system will have some initial unification-based grammar and they use learning to overcome the incompleteness of this grammar (its undergeneration, i.e. where it fails to generate strings that humans would regard as grammatical). The authors use both model-driven (deductive) and data... View full abstract»

• ### A connectionist symbol manipulator that induces rewrite rules in context-free grammars

Publication Year: 1993, Page(s):P5/1 - P5/8
| | PDF (424 KB)

A connectionist architecture is described that is able to learn to parse strings in a context-free grammar (CFG) from positive and negative examples. The architecture attempts to learn explicit rewrite rules in a grammar, to be able to reduce (or correctly parse) positive examples. This involves the ability to iteratively substitute a single nonterminal in place of a string of symbols, that is, re... View full abstract»

• ### Language understanding and subsequential transducer learning

Publication Year: 1993, Page(s):11/1 - 1110
| | PDF (720 KB)

The application of the Onward Subsequential Transducer Inference Algorithm (OSTIA) recently introduced by J. Oncina et al. (1993) to (pseudo-) natural language understanding is considered. For this purpose, a task proposed by J.A. Feldman et al. (1990), as a touchstone for comparing the capabilities of language learning systems has been adopted and three increasingly difficult semantic coding sche... View full abstract»

• ### Learning a class of regular languages in the probably approximately correct learnability framework of Valiant

Publication Year: 1993, Page(s):2/1 - 216
| | PDF (780 KB)

Results are given, relating to the probably approximately correct (M.A. Harrison, 1978) learning of a class of regular languages called terminal distinguishable regular languages. The authors prove that the VC-dimension of this concept class is infinite. However, when further restriction is imposed on the length and the structure of strings this class is found to have finite VC-dimension that grow... View full abstract»

• ### Design of artificial neural networks with a stochastic graph-L-system

Publication Year: 1993, Page(s):18/1 - 18/8
| | PDF (368 KB)

A discussion is given on the modelling of the individual development process as part of biological evolution. The purpose is to use the model to solve difficult optimization problems. The authors deal with the structuring of artificial neural networks (ANN). Among the natural models used to process information that of ANN has become one of the most used. The authors describe a model of individual ... View full abstract»