Learning a Regular Tree Language from a Teacher

Drewes, Frank; Högberg, Johanna

doi:10.1007/3-540-45007-6_22

Cited by 27 publications

(24 citation statements)

References 6 publications

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“…This result extends to deterministic bottom-up tree automata (Sakakibara, 1990;Drewes & Hogberg, 2003). Such queries, however, are not well suited for active learning of node selection queries in trees.…”

Section: Active Learning Modelmentioning

confidence: 90%

Interactive learning of node selecting tree transducer

et al. 2006

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We develop new algorithms for learning monadic node selection queries in unranked trees from annotated examples, and apply them to visually interactive Web information extraction.We propose to represent monadic queries by bottom-up deterministic Node Selecting Tree Transducers (NSTTs), a particular class of tree automata that we introduce. We prove that deterministic NSTTs capture the class of queries definable in monadic second order logic (MSO) in trees, which Gottlob and Koch (2002) argue to have the right expressiveness for Web information extraction, and prove that monadic queries defined by NSTTs can be answered efficiently. We present a new polynomial time algorithm in RPNI-style that learns monadic queries defined by deterministic NSTTs from completely annotated examples, where all selected nodes are distinguished.In practice, users prefer to provide partial annotations. We propose to account for partial annotations by intelligent tree pruning heuristics. We introduce pruning NSTTs -a formalism that shares many advantages of NSTTs. This leads us to an interactive learning algorithm for monadic queries defined by pruning NSTTs, which satisfies a new formal active learning model in the style of Angluin (1987).We have implemented our interactive learning algorithm integrated it into a visually interactive Web information extraction system-called SQUIRREL-by plugging it into the Mozilla Web browser. Experiments on realistic Web documents confirm excellent quality with very few user interactions during wrapper induction.

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Section: Active Learning Modelmentioning

confidence: 90%

Interactive learning of node selecting tree transducer

et al. 2006

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“…He considered skeletal languages, that is, the parse trees of context-free languages in which all internal nodes are unlabeled [131]. Drewes and Högberg considered regular tree languages in general [66], and proved that it is possible to avoid useless states in the inference process [67]. By doing so, the automaton A becomes a language-equivalent partial automaton with potentially exponentially fewer states.…”

Section: The Minimal Adequate Teacher Model Matmentioning

confidence: 98%

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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“…In this section, we will develop an abstract data type specification, called abstract observation table, for MAT learners [1,2,3]. The commonly used 'observation table' [1,11,12,13,19] will be an instance of this specification, but in the next section we will present another data type, called observation tree, that avoids (in our experiments) some of the coefficient queries typically asked when the learner fills the observation table.…”

Section: An Abstract Data Type For Mat Learnersmentioning

confidence: 99%

“…The principal structure of the MAT learner [11,12,13,19] is shown in Algorithm 1. Note that we only adapted it to work with our abstract observation table.…”

Section: An Abstract Data Type For Mat Learnersmentioning

confidence: 99%