Transformational and Generative Grammar

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The Basic Assumptions on Language Acquisition from Perspectives of Cognitive, Generative- Transformational, and Structural Linguistics: An Overview

The Basic Assumptions on Language Acquisition from Perspectives of Cognitive, Generative- Transformational, and Structural Linguistics: An Overview

better-grounded approach to theoretical assumptions for syntactic and semantic theory other than what generative linguistics provides (Kemmer, 2007). According to the Wikipedia, cognitive linguistics refers to a branch of linguistics that takes into account language creation, learning and usage by reference to human cognition. It is characterized by accepting three positions. First, it denies that there is an autonomous language faculty in the mind. Second, it understands grammar in terms of conceptualization, and third, it claims that knowledge of language arises out of language use. In contrast to Chomsky’s idea of modular mind, the mind does not have a module for language acquisition that is unique and autonomous; it is in contrast with generative grammar. Even though it does not deny that part of the human linguistic ability is innate, it denies that it is separate from the rest of cognition. So, knowledge of linguistics, that is, phonemes, morphemes and syntax, is conceptual in nature. It argues that the storage and retrieval of linguistic data is not different from the storage and retrieval of other kinds of knowledge, and use of language in understanding employs similar cognitive abilities as used in other non-linguistic tasks.
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Scientific modelling in generative grammar and the dynamic turn in syntax

Scientific modelling in generative grammar and the dynamic turn in syntax

It was in drawing inspiration from computability theory that the early generative tradition placed the notion of a generative grammar (subclass of a Post-canonical sys- tem or Turing machine) at the forefront of the discipline. The idea was to capture the discretely infinite set of expressions of natural language via finite means. In the Stan- dard Theory (1957–1965) or ST, phrase-structure rules performed this task. Certain rules allow for recursive structures in terms of embedding or self-reference, such as simple rules for coordination ‘N P → N P and N P ’ or more complex rules such as ‘S → N P V P’ and ‘V P → V S’ for embedded sentences. Think of this as a loop in a push-down automaton [the class of automata associated with context-free (CFG) or phrase structure grammars (PSG)] which allows for unbounded iteration and thus a dis- cretely infinite set of new expressions, e.g. Thabo is intelligent; [Thabo is intelligent] and [Thabo is brave]; [Thabo is intelligent] and [[Thabo is brave] and [Thabo is short]] etc. or ‘Sipho said that [Lungi mentioned that [Thato believed that p]]. 8 The product of the phrase structure rules (or rewrite operations) contributes to the deep structure or underlying syntactic form. This structure feeds into the transformational com- ponent of the grammar which is responsible for surface forms of expressions (through movement and deletion). ST was a progression on the purely classificatory notion of transformational grammar of Harris (1951) and Chomsky’s own more derivational view in Syntactic Structures (1957) with the addition of a lexical component or lexicon which received input from the phrase structure and inserted lexical items into the deep structure. Kernel sentences, simple declaratives devoid of any modification which can be combined to form complex sentences, were also eschewed in favour of deep struc- ture which could represent surface forms more minimally. This framework affirms the fourth tenet of the generative programme, namely the competence-performance distinction. “ST does not attempt to answer the questions of language perception and production […] rather than directly relating meaning and expression, it relates them indirectly, through deep structure.” (Langendoen 1998, p. 242). A direct relation (or determining relation) of meaning to expression is a matter of a performance grammar on this view and thus outside of the scope of linguistic theory.
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Computational Linguistics and Generative Linguistics: The Triumph of Hope over Experience

Computational Linguistics and Generative Linguistics: The Triumph of Hope over Experience

There was never any need for such attitudes. And at the conclusion of these brief remarks I will suggest a basis for thinking that relations could be much more satisfactory in the future. But I think it is worth taking a sober look at the half-century of history from 1959 to 2009, during which al- most everything about the course of theoretical syntax, at least in the USA, where I worked dur- ing the latter half of the period, has been tacitly guided by a single line of thinking. ‘Generative grammar’ is commonly used to denote it, but that will not do. First, ‘generative grammar’ is often used to mean ‘MIT-influenced transformational- generative grammar’. For that I will use the ab- breviation TG. And second, it is sometimes (in- correctly) claimed that ‘generative’ means noth- ing more or less than ‘explicit’ (see Chomsky 1966, 12: ‘a generative grammar (that is, an ex- plicit grammar that makes no appeal to the reader’s “facul´e de langage” but rather attempts to incorpo- rate the mechanisms of this faculty)’).
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Millstream Systems – a Formal Model for Linking Language Modules by Interfaces

Millstream Systems – a Formal Model for Linking Language Modules by Interfaces

Modern linguistic theories (Sadock, 1991; Jack- endoff, 2002) promote the view that different as- pects of language, such as phonology, morphol- ogy, syntax, and semantics should be viewed as autonomous modules that work simultaneously and are linked with each other by interfaces that describe their interaction and interdependency. Formalisms in modern computational linguistics which establish interfaces between different as- pects of language are the Combinatory Categorical Grammar (CCG), the Functional Generative De- scription (FGD), the Head-Driven Phrase Struc- ture Grammar (HPSG), the Lexical Functional Grammar (LFG), and the Extensible Dependency Grammar (XDG). 1 Here, we propose Millstream systems, an approach from a formal language the- oretic point of view which is based on the same ideas as XDG, but uses tree-generating modules of arbitrary kinds.
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Of Minds and Language

Of Minds and Language

At this point we have to move on to more technical discussion than is possible here, but I think it is fair to say that there has been considerable progress in moving towards principled explanation in terms of third factor considerations. The best guess about the nature of UG only a few years ago has been substantially improved by approaching the topic “from bottom up”, by asking how far we can press the strong minimalist thesis. It seems now that much of the architecture that has been postulated can be eliminated without loss, often with empirical gain. That includes the last residues of phrase structure grammar, including the notion of projection or later “labeling,” the latter perhaps eliminable in terms of minimal search. Also eliminable on principled grounds are underlying and surface structure, and also logical form, in its technical sense, leaving just the interface levels (and their existence too is not graven in stone, a separate topic). The several compositional cycles that have commonly been postulated can be reduced to one, with periodic transfer of generated structures to the interface at a few designated positions (“phases”), yielding further consequences. A very elementary form of transformational grammar essentially “comes free;” it would require stipulations to block it, so that there is a principled explanation, in these terms, for the curious but ubiquitous phenomenon of displacement in natural language, with interpretive options in positions that are phonetically silent. And by the same token, any other approach to the phenomenon carries an empirical burden. Some of the island conditions have principled explanations, as does the existence of categories for which there is no direct surface evidence, such as a functional category of inflection.
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At the Lexicon Grammar Interface: The Case of Complex Predicates in the Functional Generative Description

At the Lexicon Grammar Interface: The Case of Complex Predicates in the Functional Generative Description

In this paper, we have focused on complex predi- cates consisting of a light verb and a predicative noun. We have proposed their theoretically ad- equate and economical description based on the interplay between the grammatical and the lexi- cal components of the language description. The special attributes lvc, map and caus, comply- ing with the logical structure of the VALLEX lex- icon as well as with the main tenets of the Func- tional Generative Description, were designed. The information provided in these attributes identi- fies recurrent patterns of light verb collocations (similarly as lexical functions into which it can be easily transferred), while grammatical rules in the grammatical component generate their well- formed (both deep and surface) dependency struc- tures. We have shown how the proposed rules combine with the rules describing diatheses.
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Learning Common Grammar from Multilingual Corpus

Learning Common Grammar from Multilingual Corpus

Grammar induction using bilingual parallel cor- pora has been studied mainly in machine transla- tion research (Wu, 1997; Melamed, 2003; Eisner, 2003; Chiang, 2005; Blunsom et al., 2009; Sny- der et al., 2009). These methods require sentence- aligned parallel data, which can be costly to obtain and difficult to scale to many languages. On the other hand, our model does not require sentences to be aligned. Moreover, since the complexity of our model increases linearly with the number of languages, our model is easily applicable to cor-

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FULL ISSUE

FULL ISSUE

early scientific revolution distinguished universal from particular grammar, though not in the biolinguistic sense. Universal grammar was taken to be the intellectual core of the discipline; particular grammars are accidental instantiations. With the flourishing of anthropological linguistics, the pendulum swung in the other direction, towards diversity, well captured in the Boasian formulation to which I referred. In general biology, a similar issue had been raised sharply in the Cuvier–Geoffroy debate in 1830 (Appel 1987). Cuvier’s position, emphasizing diversity, prevailed, particularly after the Darwinian revolution, leading to the conclusions about near infinitude of variety that have to be sorted out case by case, which I mentioned earlier. Perhaps the most quoted sentence in biology is Darwin’s final observation in Origin of Species about how “from so simple a beginning, endless forms most beautiful and most wonderful have been, and are being, evolved.” I don’t know if the irony was intended, but these words were taken by Sean Carroll (2005) as the title of his introduction to The New Science of Evo Devo, which seeks to show that the forms that have evolved are far from endless, in fact are remarkably uniform, presumably, in important respects, because of factors of the kind that Thompson and Turing thought should constitute the true science of biology. The uniformity had not passed unnoticed in Darwin’s day. Thomas Huxley’s naturalistic studies led him to observe that there appear to be “predetermined lines of modification” that lead natural selection to “produce varieties of a limited number and kind” for each species. 2
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CONTENTS

CONTENTS

early scientific revolution distinguished universal from particular grammar, though not in the biolinguistic sense. Universal grammar was taken to be the intellectual core of the discipline; particular grammars are accidental instantiations. With the flourishing of anthropological linguistics, the pendulum swung in the other direction, towards diversity, well captured in the Boasian formulation to which I referred. In general biology, a similar issue had been raised sharply in the Cuvier–Geoffroy debate in 1830 (Appel 1987). Cuvier’s position, emphasizing diversity, prevailed, particularly after the Darwinian revolution, leading to the conclusions about near infinitude of variety that have to be sorted out case by case, which I mentioned earlier. Perhaps the most quoted sentence in biology is Darwin’s final observation in Origin of Species about how “from so simple a beginning, endless forms most beautiful and most wonderful have been, and are being, evolved.” I don’t know if the irony was intended, but these words were taken by Sean Carroll (2005) as the title of his introduction to The New Science of Evo Devo, which seeks to show that the forms that have evolved are far from endless, in fact are remarkably uniform, presumably, in important respects, because of factors of the kind that Thompson and Turing thought should constitute the true science of biology. The uniformity had not passed unnoticed in Darwin’s day. Thomas Huxley’s naturalistic studies led him to observe that there appear to be “predetermined lines of modification” that lead natural selection to “produce varieties of a limited number and kind” for each species. 2
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Alternative Paradigm for Language  Acquisition

Alternative Paradigm for Language Acquisition

The Mentalist paradigm emphasizes the role of the mind in the cognitive process (Chomsky, 1995: pp. 47-55). The mind and the brain are two realities of different substances, the first is spiritual and the second is physical. The cognition is processed through the inborn rules hypothesize through the representation of the world in the mind by serial processing of abstract and fixed symbols (Poersch, 2005: p. 165). Chomsky believes that this process proposes the inborn existence of the mind through the cognitive process that has been influenced by the surrounding. The brain contains thousands of neurons connected and constituted of a body and two kinds of fi- laments that are responsible for the net formation; the axon and the synapse. The axon is electrical transmitters connecting a neuron body to synapse and other neurons. When an axon reaches a dendrite, there is a space in which chemical reactions are processed. The synapse reactions are responsible for learning process (Poersch, 2005: p. 168). As a result of the above case, the synthesis of philosophical grammar and structural linguistics that were introduced by mentalist begin to refute the behavioral sciences that are not sciences of the mind, avoiding the metaphysics issues, but discover the procedures apparently. Language is the mirror of the mind, it constructs data with innate property, then called “universal grammar”. Thus, Chomsky believed that to under- stand the deep structure in generative grammar, the mental process is needed in order to explain the evolution of human language (Chomsky, 2006: pp. 107-108).
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Stop probability estimates computed on a large corpus improve Unsupervised Dependency Parsing

Stop probability estimates computed on a large corpus improve Unsupervised Dependency Parsing

Other approaches to unsupervised dependency parsing were described e.g. in (Søgaard, 2011), (Cohen et al., 2011), and (Bisk and Hockenmaier, 2012). There also exist “less unsupervised” ap- proaches that utilize an external knowledge of the POS tagset. For example, Rasooli and Faili (2012) identify the last verb in the sentence, minimize its probability of reduction and thus push it to the root position. Naseem et al. (2010) make use of manually-specified universal dependency rules such as Verb→Noun, Noun→Adjective. McDon- ald et al. (2011) identify the POS tags by a cross- lingual transfer. Such approaches achieve better results; however, they are useless for grammar in- duction from plain text.
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Data Recombination for Neural Semantic Parsing

Data Recombination for Neural Semantic Parsing

Modeling crisp logical regularities is cru- cial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. In this paper, we introduce data recom- bination, a novel framework for inject- ing such prior knowledge into a model. From the training data, we induce a high- precision synchronous context-free gram- mar, which captures important conditional independence properties commonly found in semantic parsing. We then train a sequence-to-sequence recurrent network (RNN) model with a novel attention-based copying mechanism on datapoints sam- pled from this grammar, thereby teaching the model about these structural proper- ties. Data recombination improves the ac- curacy of our RNN model on three se- mantic parsing datasets, leading to new state-of-the-art performance on the stan- dard GeoQuery dataset for models with comparable supervision.
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Phrase Translation Probabilities with ITG Priors and Smoothing as Learning Objective

Phrase Translation Probabilities with ITG Priors and Smoothing as Learning Objective

In this paper we also start out from a stan- dard phrase extraction procedure based on word- alignment and aim solely at estimating the condi- tional probabilities for the phrase pairs and their reverse translation probabilities. Unlike preceding work, we extract all phrase pairs from the training corpus and estimate their probabilities, i.e., without limit on length. We present a novel formulation of a conditional translation model that works with a prior over segmentations and a bag of conditional phrase pairs. We use binary Synchronous Context- Free Grammar (bSCFG), based on Inversion Trans- duction Grammar (ITG) (Wu, 1997; Chiang, 2005a), to define the set of eligible segmentations for an aligned sentence pair. We also show how the num- ber of spurious derivations per segmentation in this bSCFG can be used for devising a prior probabil- ity over the space of segmentations, capturing the bias in the data towards monotone translation. The heart of the estimation process is a new smoothing estimator, a penalized version of Deleted Estima- tion, which averages the temporary probability es- timates of multiple parallel EM processes at each joint iteration.
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An Analysis of Researches on Nominalization by Major Linguistics Schools

An Analysis of Researches on Nominalization by Major Linguistics Schools

According to Bussmann (2000), nominalization refers to a creative formation process through which words of all parts of speech could be converted into nouns, in other words, nominalization is a transformation form other word-classes to nouns. Actually, in Bussmann’s opinion, nominalization is mainly transferred form verbs and adjectives, because this transformational process is relatively easier in comparison with other parts of speech, such as conjunctions or prepositions. Matthews holds the same opinion like Bussmann (2000: 244) who considered nominalization as any process by which either a noun or a syntactic unit functioning as a noun phrase, or any noun which is derived from any other kind of unit such as a verb or an adjective. Similarly, Biber (1998) believed that nominalization is nouns that are transformed from other word classes, mainly from verbs and adjectives, which means nominalized words are derived from adding suffixes to verbs and adjectives. As a whole, from the aforementioned definitions given by Bussmann, Matthews and Biber, it can be concluded that they all considered nominalization as a kind of transformational process which involves changes of word classes, especially transferred from verbs and adjectives (Biber et al 2000; Crystal 2002). However, these definitions still rest on lexical level, and the transformational process is restricted to verbs and adjectives, hence, these definitions are somewhat limited and superficial (Banks 2005).
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An Overview of Generative Grammar in Albanian

An Overview of Generative Grammar in Albanian

The Terminology Glossaries are composed of compound terminology units or composite terminology like terms for phrases in generative grammar which are the most commonly used means in the construction of scientific terms by linguists. When dealing with terminology we encounter the phenomenon of the introduction of a foreign terminology in the Albanian language, such as: actant, advectiv, adjuksionet, cirkostantë, konstituente-bërthamë, konstituente- jobërthamë.(Koleci,2011).The greatest contribution in the process of cleaning and enriching the Albanian terminology in the field of linguistics was given by the translators of the works written by the founders of this new syntactic direction and the Albanian linguists such as Mita Joseph, Rahmi Memushaj who in their works have used the translated terminology instead of the borrowed terminology. Some examples of the cleaning process: drejtim nga koka (en. Head government); elemente ndihmëse (en. Auxiliary); eptimi (angl.Flection); kallëzuesia (en. predicate); lëvizje të fshehura (en. COVERT movement). Examples of the enriching process: përngjitje (en. agglutination); përemra shënues (en. Espletivet); periudha (angl.Complex sentence); pranueshmëria (angl.Acceptability); rimarrja (en. Anaphora).(Graffi,2008). The translated terms help the reader understand the content of the terms and remember them easier when the new theories of generative grammar are discussed given that this theory has been introduced very rapidly in the Albanian language. Specifically: roli tematik i jashtëm; rregullat e strukturës së frazës; struktura e kllapave të etiketuara; struktura e sintagmës emërore; rrangu kryesor i pjesëve të ligjëratës (the external thematic role; the phrase structure rules, the structure of labeled brackets; syntagmatic structure of names; the principal rank of the discourse parts). However, in the course of the time, the extended linguistic units can be shortened in the expression of the concepts until to the size of a simple phrase (for e.g. the rules of nominalization / nominalization), or until to a compound word (e.g., adjective: adverb (ndajfolje) + adjective (mbiemer).
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A Probabilistic Generative Grammar for Semantic Parsing

A Probabilistic Generative Grammar for Semantic Parsing

Our grammar formalism can be related to synchronous CFGs (SCFGs) (Aho and Ull- man, 1972) where the semantics and syntax are generated simultaneously. However, in- stead of modeling the joint probability of the logical form and natural language utterance p(x, y), we model the factorized probability p(x)p(y|x). Modeling each component in isola- tion provides a cleaner division between syntax and semantics, and one half of the model can be modified without affecting the other (such as the addition of new background knowl- edge, or changing the language/semantic for- malism). We used a CFG in the syntactic portion of our model (although our grammar is not context-free, due to the dependence on the logical form). Richer syntactic for- malisms such as combinatory categorial gram- mar (Steedman, 1996) or head-driven phrase structure grammar (Pollard and Sag, 1994) could replace the syntactic component in our framework and may provide a more uniform analysis across languages. Our model is simi- lar to lexical functional grammar (LFG) (Ka-
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Generative Models for Statistical Parsing with Combinatory Categorial Grammar

Generative Models for Statistical Parsing with Combinatory Categorial Grammar

and tested on a translation of the Penn Treebank to a corpus of CCG normal-form derivations. CCG grammars are characterized by much larger category sets than standard Penn Treebank grammars, distin- guishing for example between many classes of verbs with different subcategorization frames. As a re- sult, the categorial lexicon extracted for this purpose from the training corpus has 1207 categories, com- pared with the 48 POS-tags of the Penn Treebank. On the other hand, grammar rules in CCG are lim- ited to a small number of simple unary and binary combinatory schemata such as function application and composition. This results in a smaller and less overgenerating grammar than standard PCFGs (ca. 3,000 rules when instantiated with the above cate- gories in sections 02-21, instead of > 12,400 in the
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