Error-correcting tree language inference

López, Damián; España, Salvador

doi:10.1016/s0167-8655(01)00095-2

Cited by 16 publications

(5 citation statements)

References 9 publications

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“…In the future, we will try to compare our work with other works on the inference of tree languages and of context-free languages, as they are contained, e.g., in [5,27,30,33,43,47,48,61,[64][65][66][71][72][73]. Moreover, it would be interesting to extend the work to other, more general classes of tree languages and the corresponding languages of yielded strings, see [70] for a short exposition.…”

Section: Discussion and Prospectsmentioning

confidence: 99%

Learning tree languages from text

Fernau

2007

RAIRO-Theor. Inf. Appl.

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We study the problem of learning regular tree languages from text. We show that the framework of function distinguishability, as introduced by the author in [Theoret. Comput. Sci. 290 (2003) 1679-1711], can be generalized from the case of string languages towards tree languages. This provides a large source of identifiable classes of regular tree languages. Each of these classes can be characterized in various ways. Moreover, we present a generic inference algorithm with polynomial update time and prove its correctness. In this way, we generalize previous works of Angluin, Sakakibara and ourselves. Moreover, we show that this way all regular tree languages can be approximately identified.

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Section: Discussion and Prospectsmentioning

confidence: 99%

Learning tree languages from text

Fernau

2007

RAIRO-Theor. Inf. Appl.

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“…Another way of handling errors is that of error-correcting grammatical inference (ECGI). This idea was introduced by Rulot [179,180] for string languages and extended to tree languages by López and his co-authors [136][137][138]. In short, ECGI provides a learning model that is robust against certain errors.…”

Section: Error-correcting Grammatical Inference (Ecgi)mentioning

confidence: 99%

Learning Tree Languages

Björklund

Fernau

2016

Topics in Grammatical Inference

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Tree languages have proved to be a versatile and rewarding extension of the classical notion of string languages. Many nice applications have been established over the years, in areas such as Natural Language Processing, Information Extraction, and Computational Biology. Although some properties of string languages transfer easily to the tree case, in particular for regular languages, several computational aspects turn out to be harder. It is therefore both of theoretical and of practical interest to investigate how far and in what ways Grammatical Inference algorithms developed for the string case are applicable to trees. This chapter surveys known results in this direction. We begin by recalling the basics of tree language theory. Then, the most popular learning scenarios and algorithms are presented. Several applications of Grammatical Inference of tree languages are reviewed in some detail. We conclude by suggesting a number of directions for future research.

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“…These representations have shown to be useful in pattern recognition tasks [10] [11]. In order to capture only the more relevant features of the images, the algorithm we propose takes into account a reduced image.…”

Section: For Each Node (And Its Corresponding Quadrant)mentioning

confidence: 99%

“…Briefly, this method measures the importance of each of the components of the image and then represents the relative position of each component using tree-like components [7]. Once the documents are reduced to a tree-like structure, this work uses a tree language inference algorithm [10] which builds a tree automaton for each class of the problem using a minimum edition distance criterion.…”

Section: Introductionmentioning

confidence: 99%

Syntactic Modeling and Recognition of Document Images

Perea

López

2004

Lecture Notes in Computer Science

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Abstract. In this work, we propose a new scheme for the recognition of document images under a syntactic approach. We present a new method to model the layout of the document using a tree-like representation of the form. The syntactic representation of the documents are used to infer a tree automaton for each one of the classes involved in the task. An error-correcting analysis of tree languages allows us to carry out the classification. The experimentation carried out showed the good behaviour of the approach: error rate of 1.18%.

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Error-correcting tree language inference

Cited by 16 publications

References 9 publications

Learning tree languages from text

Learning tree languages from text

Learning Tree Languages

Syntactic Modeling and Recognition of Document Images

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