Cross-Serial Dependencies in Dutch

Bresnan, Joan; Kaplan, Ronald M.; Peters, Stanley; Zaenen, Annie

doi:10.1007/978-94-009-3401-6_11

Cited by 102 publications

(85 citation statements)

References 6 publications

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“…By that time, Bresnan et al [10], Culy [15] and Shieber [31] presented some clear examples of natural language structures that cannot be described using a context-free grammar. Such examples were found in three different natural languages: Dutch, Bambara and Swiss-German.…”

Section: Location Of Natural Languages In the Chomsky Hierarchymentioning

confidence: 99%

Bio-inspired Grammatical Inference

Becerra-Bonache

2011

Lecture Notes in Computer Science

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Abstract. The field of Grammatical Inference was originally motivated by the problem of natural language acquisition. However, the formal models proposed within this field have left aside this linguistic motivation. In this paper, we propose to improve models and techniques used in Grammatical Inference by using ideas coming from linguistic studies. In that way, we try to give a new bio-inspiration to this field.

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Section: Location Of Natural Languages In the Chomsky Hierarchymentioning

confidence: 99%

Bio-inspired Grammatical Inference

Becerra-Bonache

2011

Lecture Notes in Computer Science

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“…It seems that neither the family of regular or context-free languages have enough expressiveness to describe the basic contextsensitive syntactic constructions found in natural languages. Several attempts have been made to prove the non-context-freeness of natural languages [6,7]. Despite the fact that the non-context-freeness of natural language has become the standardly accepted theory, there are linguists such as Pullum and Gazdar who, after reviewing the various attempts to establish that natural languages are not context-free, come to the conclusion that every published argument purporting to demonstrate the non-context-freeness of some natural language is invalid, either formally or empirically or both [8].…”

Section: The Language To Be Learnedmentioning

confidence: 99%

Children as Models for Computers: Natural Language Acquisition for Machine Learning

Becerra-Bonache¹,

Jiménez-López²

2011

Proceedings of the 1st International Workshop on AI Methods for Interdisciplinary Research in Language and Biology

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Abstract. This paper focuses on a subfield of machine learning, the socalled grammatical inference. Roughly speaking, grammatical inference deals with the problem of inferring a grammar that generates a given set of sample sentences in some manner that is supposed to be realized by some inference algorithm. We discuss how the analysis and formalization of the main features of the process of human natural language acquisition may improve results in the area of grammatical inference.

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“…The extent of dependencies in English auxiliary constructions is bounded, but we can get unbounded crossing dependencies between subjects and verbs in Dutch constructions like the following (Bresnan, Kaplan, Peters, & Zaenen, 1982;Huybregts, 1976):…”

Section: The Historical Setting Brieflymentioning

confidence: 99%

Varieties of crossing dependencies: structure dependence and mild context sensitivity

Stabler

2004

Cognitive Science

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Four different kinds of grammars that can define crossing dependencies in human language are compared here: (i) context sensitive rewrite grammars with rules that depend on context, (ii) matching grammars with constraints that filter the generative structure of the language, (iii) copying grammars which can copy structures of unbounded size, and (iv) generating grammars in which crossing dependencies are generated from a finite lexical basis. Context sensitive rewrite grammars are syntactically, semantically and computationally unattractive. Generating grammars have a collection of nice properties that ensure they define only "mildly context sensitive" languages, and Joshi has proposed that human languages have those properties too. But for certain distinctive kinds of crossing dependencies in human languages, copying or matching analyses predominate. Some results relevant to the viability of mildly context sensitive analyses and some open questions are reviewed.

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Cross-Serial Dependencies in Dutch

Cited by 102 publications

References 6 publications

Bio-inspired Grammatical Inference

Bio-inspired Grammatical Inference

Children as Models for Computers: Natural Language Acquisition for Machine Learning

Varieties of crossing dependencies: structure dependence and mild context sensitivity

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