Looking for an easy-to-use guide to English grammar? This handy introduction covers all the basics of the subject, using a simple and straightforward style. Students will find the book's step-by-step approach easy to follow and be encouraged by its non-technical language. Requiring no prior knowledge of English grammar, the information is presented in small steps, with objective techniques to help readers apply new concepts.
Oxford Living Grammar takes a practical approach to grammar. The four-page units provide clear explanations and information on when the grammar is used, followed by extensive practice. Each level includes an interactive CD-ROM.
■Explanations include a ‘Grammar in action’ section which explains when the grammar is typically used.
■Exercises are contextualized so that students practise using the grammar in everyday situations.
■’Word focus’ boxes highlight interesting idiomatic expressions or words students might not be familiar with.
In this paper we compare different approaches to extract deﬁnitions of four types using a combination of a rule-based grammar and machine learning. We collected a Dutch text corpus containing 549 deﬁnitions and applied a grammar on it. Machine learning was then applied to improve the results obtained with the grammar. Two machine learning experiments were carried out. In the ﬁrst experiment, a standard classiﬁer and a classiﬁer designed speciﬁcally to deal with imbalanced datasets are compared.
This paper introduces a method for the semi-automatic generation of grammar test items by applying Natural Language Processing (NLP) techniques. Based on manually-designed patterns, sentences gathered from the Web are transformed into tests on grammaticality. The method involves representing test writing knowledge as test patterns, acquiring authentic sentences on the Web, and applying generation strategies to transform sentences into items.
Nonconcatenative constraints, such as the shuffle relation, are frequently employed in grammatical analyses of languages that have more flexible ordering of constituents than English. We show how it is possible to avoid searching the large space of permutations that results from a nondeterministic application of shuffle constraints. The results of our implementation demonstrate that deterministic application of shuffle constraints yields a dramatic improvement in the overall performance of a head-corner parser for German using an HPSG-style grammar.
This paper proposes a method of correcting annotation errors in a treebank. By using a synchronous grammar, the method transforms parse trees containing annotation errors into the ones whose errors are corrected. The synchronous grammar is automatically induced from the treebank. We report an experimental result of applying our method to the Penn Treebank.
We propose a language model based on a precise, linguistically motivated grammar (a hand-crafted Head-driven Phrase Structure Grammar) and a statistical model estimating the probability of a parse tree. The language model is applied by means of an N-best rescoring step, which allows to directly measure the performance gains relative to the baseline system without rescoring. To demonstrate that our approach is feasible and beneﬁcial for non-trivial broad-domain speech recognition tasks, we applied it to a simpliﬁed German broadcast-news transcription task.
Log-linear models provide a statistically sound framework for Stochastic "Unification-Based" Grammars (SUBGs) and stochastic versions of other kinds of grammars. We describe two computationally-tractable ways of estimating the parameters of such grammars from a training corpus of syntactic analyses, and apply these to estimate a stochastic version of LexicalFunctional Grammar.
We present a stochastic parsing system consisting of a Lexical-Functional Grammar (LFG), a constraint-based parser and a stochastic disambiguation model. We report on the results of applying this system to parsing the UPenn Wall Street Journal (WSJ) treebank. The model combines full and partial parsing techniques to reach full grammar coverage on unseen data. The treebank annotations are used to provide partially labeled data for discriminative statistical estimation using exponential models.
A system is described for acquiring a contextsensitive, phrase structure g r a m m a r which is applied by a best-path, bottom-up, deterministic parser. The grammar was based on English news stories and a high degree of success in parsing is reported. Overall, this research concludes that CSG is a computationally and conceptually tractable approach to the construction of phrase structure g r a m m a r for news story text. 1 A context-free g r a m m a r production is characterized as a rewrite rule where a non-terminal element as a leftside...
In this paper we describe a new technique for parsing free text: a transformational grammar I is automatically learned that is capable of accurately parsing text into binary-branching syntactic trees with nonterminals unlabelled. The algorithm works by beginning in a very naive state of knowledge about phrase structure. By repeatedly comparing the results of bracketing in the current state to proper bracketing provided in the training corpus, the system learns a set of simple structural transformations that can be applied to reduce error.
A grammatical description often applies to a linguistic object only when that object has certain features. Such conditional descriptions can be indirectly modeled in Kay's Functional Unification Grammar (FUG) using functional descriptions that are embedded within disjunctive alternatives. An extension to FUG is proposed that allows for a direct representation of conditional descriptions. This extension has been used to model the input conditions on the systems of systemic grammar. Conditional descriptions are formally defined in terms of logical implication and negation.
A novel formalism is presented for Earley-like parsers. It accommodates the simulation of non-deterministic pushdown automata. In particular, the theory is applied to non-deterministlc LRoparsers for RTN grammars. A major problem of computational linguistics is the inefficiency of parsing natural language. The most popular parsing method for context-free natural language grammars, is the genera/ context-free parsing method of Earley . It was noted by Lang , that Earley-like methods can be used for simulating a class of non-determlnistic pushdown antomata(NPDA).
A new approach to bottom-up parsing that extends Augmented Context-Free Grammar to a Process Grammar is formally presented. A Process Grammar (PG) defines a set of rules suitedfor bottom-up parsing and conceived as processes that are applied by a P G Processor. The matching phase is a crucial step for process application, and a parsing structure for efficient matching is also presented. The PG Processor is composed of a process scheduler that allows immediate constituent analysis of structures, and behaves in a non-deterministic fashion. ...
In this paper1 we introduce eXtensible MetaGrammar, a system that facilitates the development of tree based grammars. This system includes both (1) a formal language adapted to the description of linguistic information and (2) a compiler for this language. It applies techniques of logic programming (e.g. Warren’s Abstract Machine), thus providing an efﬁcient and theoretically motivated framework for the processing of linguistic metadescriptions.
Myanmar language and script are unique and complex. Up to our knowledge, considerable amount of work has not yet been done in describing Myanmar script using formal language theory. This paper presents manually constructed context free grammar (CFG) with “111” productions to describe the Myanmar Syllable Structure. We make our CFG in conformity with the properties of LL(1) grammar so that we can apply conventional parsing technique called predictive top-down parsing to identify Myanmar syllables. We present Myanmar syllable structure according to orthographic rules. ...
We present evidence that head-driven parsing strategies lead to efficiency gains over standard parsing strategies, for lexicalist, concatenative and unification-based grammars. A head-driven parser applies a rule only after a phrase matching the head has been derived. By instantiating the head of the rule important information is obtained about the left-hand-side and the other elements of the right-hand-side. We have used two different head-driven parsers and a number of standard parsers to parse with lexicalist grammars for English and for Dutch. ...
In this paper, we present a new collection of open-source software libraries that provides command line binary utilities and library classes and functions for compiling regular expression and context-sensitive rewrite rules into ﬁnite-state transducers, and for n-gram language modeling. The OpenGrm libraries use the OpenFst library to provide an efﬁcient encoding of grammars and general algorithms for building, modifying and applying models.
We propose the use of regular tree grammars (RTGs) as a formalism for the underspeciﬁed processing of scope ambiguities. By applying standard results on RTGs, we obtain a novel algorithm for eliminating equivalent readings and the ﬁrst efﬁcient algorithm for computing the best reading of a scope ambiguity. We also show how to derive RTGs from more traditional underspeciﬁed descriptions.
This paper evaluates the LinGO Grammar Matrix, a cross-linguistic resource for the development of precision broad coverage grammars, by applying it to the Australian language Wambaya. Despite large typological differences between Wambaya and the languages on which the development of the resource was based, the Grammar Matrix is found to provide a signiﬁcant jump-start in the creation of the grammar for Wambaya: With less than 5.5 person-weeks of development, the Wambaya grammar was able to assign correct semantic representations to 76% of the sentences in a naturally occurring text. ...