610,989 research outputs found

    Compacting the Penn Treebank Grammar

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    Treebanks, such as the Penn Treebank (PTB), offer a simple approach to obtaining a broad coverage grammar: one can simply read the grammar off the parse trees in the treebank. While such a grammar is easy to obtain, a square-root rate of growth of the rule set with corpus size suggests that the derived grammar is far from complete and that much more treebanked text would be required to obtain a complete grammar, if one exists at some limit. However, we offer an alternative explanation in terms of the underspecification of structures within the treebank. This hypothesis is explored by applying an algorithm to compact the derived grammar by eliminating redundant rules -- rules whose right hand sides can be parsed by other rules. The size of the resulting compacted grammar, which is significantly less than that of the full treebank grammar, is shown to approach a limit. However, such a compacted grammar does not yield very good performance figures. A version of the compaction algorithm taking rule probabilities into account is proposed, which is argued to be more linguistically motivated. Combined with simple thresholding, this method can be used to give a 58% reduction in grammar size without significant change in parsing performance, and can produce a 69% reduction with some gain in recall, but a loss in precision.Comment: 5 pages, 2 figure

    First-language grammar in the classroom : from consciousness raising to learner autonomy

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    According to students grammar lessons are boring and tedious. If you ask them why they will tell you that almost all they do in grammar lessons is to study and to practice 'rules' (see Micallef 1995). When asked how they feel about learning 'grammar', Form 3 students at a Juniour Lyceum stated that grammar " ... tad-dwejjaq, fiha qabda regoli, u li fiha ma nifhmu xejn. Kollox trid tistudja bl-amment ghall-eiami" (it is tedious, full of rules that we do not understand. Everything has to be studied for the exam). When asked why they think they should learn grammar they replied that without it "ma niktbux Malti tajjeb u importanti ghax tkun fl-eiamf' (we cannot write Maltese correctly, and it is important for the exam). Form 1 students were also asked to give their opinion about grammar and grammar lessons. They think that they need to study grammar "biex nispellu tajjelf' (to spell correctly); and that grammar is" dik li toqghod taghmel hafna jien, int, huwa, hija. Konna ndum u nimlew pages fil-Year 6 biex ghamilna tal-junior!" (full of conjugations. We used to fill pages of them when we were preparing to sit for the 11 + examination). Little do they know that as native speakers they make constant use of grammar in their everyday communication!peer-reviewe

    A matter of time: Implicit acquisition of recursive sequence structures

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    A dominant hypothesis in empirical research on the evolution of language is the following: the fundamental difference between animal and human communication systems is captured by the distinction between regular and more complex non-regular grammars. Studies reporting successful artificial grammar learning of nested recursive structures and imaging studies of the same have methodological shortcomings since they typically allow explicit problem solving strategies and this has been shown to account for the learning effect in subsequent behavioral studies. The present study overcomes these shortcomings by using subtle violations of agreement structure in a preference classification task. In contrast to the studies conducted so far, we use an implicit learning paradigm, allowing the time needed for both abstraction processes and consolidation to take place. Our results demonstrate robust implicit learning of recursively embedded structures (context-free grammar) and recursive structures with cross-dependencies (context-sensitive grammar) in an artificial grammar learning task spanning 9 days. Keywords: Implicit artificial grammar learning; centre embedded; cross-dependency; implicit learning; context-sensitive grammar; context-free grammar; regular grammar; non-regular gramma

    From left-regular to Greibach normal form grammars

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    Each context-free grammar can be transformed to a context-free grammar in Greibach normal form, that is, a context-free grammar where each right-hand side of a prorfuction begins with a terminal symbol and the remainder of the right-hand side consists of nonterminal symbols. In this short paper we show that for a left-regular grammar G we can obtain a right-regular grammar G’ (which is by definition in Greibach normal form) which left-to-right covers G (in this case left parses of G’ can be mapped by a homomorphism on right parses of G. Moreover, it is possible to obtain a context-free grammar G” in Greibach normal form which right covers the left-regular grammar G (in this case right parses of G” are mapped on right parses of G)

    Organisation and Contents of Korean Pedagogical Grammar - With focus on Korean: A Comprehensive Grammar (Yeon & Brown)

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    This paper aims to discuss how Korean pedagogical grammar should be written in terms of organisation and description of content. The arguments in this paper will be presented in practical and empirical manners rather than theoretical ones. The problematic questions and empirical issues presented arose while the author was writing a pedagogical grammar book entitled ‘Korean: A Comprehensive Grammar’, published by Routledge in early 2011. The point about pedagogical grammar is that it is not the same as linguistic grammar because they have different functions and uses. Pedagogical grammar typically requires rules that are definite, coherent, consistent,non-technical,cumulative and heuristic. Actual problems and topics at issue are discussed in the paper and the book’s table of contents is presented at the end of the paper

    A collective extension of relational grammar

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    Relational grammar was proposed in Suppes (1976) as a semantical grammar for natural language. Fragments considered so far are restricted to distributive notions. In this article, relational grammar is extended to collective notions

    Grammar induction for mildly context sensitive languages using variational Bayesian inference

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    The following technical report presents a formal approach to probabilistic minimalist grammar induction. We describe a formalization of a minimalist grammar. Based on this grammar, we define a generative model for minimalist derivations. We then present a generalized algorithm for the application of variational Bayesian inference to lexicalized mildly context sensitive language grammars which in this paper is applied to the previously defined minimalist grammar

    On the covering of left recursive grammars

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    In this paper we show that some prevailing ideas on the elimination of left recursion in a context-free grammar are not valid. An algorithm and a proof are given to show that every proper context-free grammar is covered by a non-left-recursive grammar
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