Natural language parsing

Xem 1-20 trên 273 kết quả Natural language parsing
  • A general device for handling nondeterminism in stack operations is described. The device, called a Graph-structured Stack, can eliminate duplication of operations throughout the nondeterministic processes. This paper then applies the graph-structured stack to various natural language parsing methods, including ATN, LR parsing, categodal grammar and principlebased parsing. The relationship between the graphstructured stack and a chart in chart parsing is also discussed.

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  • It is necessary to have a (large) annotated corpus to build a statistical parser. Acquisition of such a corpus is costly and time-consuming. This paper presents a method to reduce this demand using active learning, which selects what samples to annotate, instead of annotating blindly the whole training corpus. Sample selection for annotation is based upon “representativeness” and “usefulness”. A model-based distance is proposed to measure the difference of two sentences and their most likely parse trees.

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  • We derive the rhetorical structures of texts by means of two new, surface-form-based algorithms: one that identifies discourse usages of cue phrases and breaks sentences into clauses, and one that produces valid rhetorical structure trees for unrestricted natural language texts. The algorithms use information that was derived from a corpus analysis of cue phrases.

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  • Acquiring information systems specifications from natural language description is presented as a problem class that requires a different treatment of semantics when compared with other applied NL systems such as database and operating system interfaces. Within this problem class, the specific task of obtaining explicit conceptual data models from natural language text or dialogue is being investigated. The knowledge brought to bear on this task is classified into syntactic, semantic and systems analysis knowledge.

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  • In this paper* I will argue for a model of grammatical processing that is based on uniform processing and knowledge sources. The main feature of this model is to view parsing and generation as two strongly interleaved tasks performed by a single parametrized deduction process. It will be shown that this view supports flexible and efficient natural language processing.

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  • Introduction: Most research in machine learning has been focused on binary classification, in which the learned classifier outputs one of two possible answers. Important fundamental questions can be analyzed in terms of binary classification, but realworld natural language processing problems often involve richer output spaces. In this tutorial, we will focus on classifiers with a large number of possible outputs with interesting structure. Notable examples include information retrieval, part-of-speech tagging, NP chucking, parsing, entity extraction, and phoneme recognition. ...

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  • We present a natural language interface system which is based entirely on trained statistical models. The system consists of three stages of processing: parsing, semantic interpretation, and discourse. Each of these stages is modeled as a statistical process. The models are fully integrated, resulting in an end-to-end system that maps input utterances into meaning representation frames.

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  • This paper introduces a special programming environment for the definition of grammars and for the implementation of corresponding parsers. In natural language processing systems it is advantageous to have linguistic knowledge and processing mechanisms separated. Our environment accepts grammars consisting of binary dependency relations and grammatical functions. Well-formed expressions of functions and relations provide constituent surroundings for syntactic categories in the form of two-way automata. ...

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  • This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) estimation with L2 regularization, the Averaged Perceptron (AP), and Boosting. We also investigate ME estimation with L1 regularization using a novel optimization algorithm, and BLasso, which is a version of Boosting with Lasso (L1) regularization. We first investigate all of our estimators on two re-ranking tasks: a parse selection task and a language model (LM) adaptation task. ...

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  • The purpose of this paper is to examine the oft-repeated assertion regarding the efficiency of a "simple parsing algorithm" combinable with a variety of different grammars written in the form of appropriate tables of rules.

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  • Previous research applying kernel methods to natural language parsing have focussed on proposing kernels over parse trees, which are hand-crafted based on domain knowledge and computational considerations. In this paper we propose a method for defining kernels in terms of a probabilistic model of parsing. This model is then trained, so that the parameters of the probabilistic model reflect the generalizations in the training data. The method we propose then uses these trained parameters to define a kernel for reranking parse trees. ...

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  • Natural language parsing is conceived to be a procedure of disambiguation, which successively reduces an initially totally ambiguous structural representation towards a single interpretation. Graded constraints are used as means to express wellformedness conditions of different strength and to decide which partial structures are locally least preferred and, hence, can be deleted. This approach facilitates a higher degree of robustness of the analysis, allows to introduce resource adaptivity into the parsing procedure, and exhibits a high potential for parallelization of the computation. ...

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  • This paper proposes a welcome hypothesis: a computationally simple device z is sufficient for processing natural language. Traditionally it has been argued that processing natural language syntax requires very powerful machinery. Many engineers have come to this rather grim conclusion; almost all working parers are actually Turing Machines (TM).

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  • Nowhere is t h e tension between the two areas of our field--computatlon and llnguistlcs--more apparent than in the issues that arise in connection with parsing natural language input. This panel addresses those issues from both computational and linguisric perspectives. Each panelist has submitted a position paper on some of the questions that appear below. The questions are loosely grouped in three sections. The first concentrates on the computational aspect, the second on the linguistic aspect, and the third on their interactions. ...

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  • Robust natural language interpretation requires strong semantic domain models, "fall-soff" recovery heuristics, and very flexible control structures. Although single-strategy parsers have met with a measure of success, a multi.strategy approach is shown to provide a much higher degree of flexibility, redundancy, and ability to bring task-specific domain knowledge (in addition to general linguistic knowledge) to bear on both grammatical and ungrammatical input. A parsing algorithm is presented that integrates several different parsing strategies, with case-frame instantiation dominating. ...

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  • The paper begins by defining a class of distributed memory machines which have useful properties as retrieval and filtering devices. These memory mechanisms store large numbers of associations on a single composite vector. They provide a natural format for encoding the syntactic and semantic constraints associated with linguistic elements. A computational architecture for parsing natural language is proposed which utillses the retrieval and associative features of these devices.

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  • The Head-driven Phrase Structure G r a m m a r project ( H P S G ) is an English language database query system under development at Hewlett-Packard Laboratories. Unlike other product-oriented efforts in the natural language understanding field, the H P S G system was designed and implemented by linguists on the basis of recent theoretical developments. But, unlike other implementations of linguistic theories, this system is not a toy, as it deals with a variety of practical problems not covered in the theoretical literature. ...

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  • The Context-Free backbone of some natural language analyzers produces all possible CF parses as some kind of shared forest, from which a single tree is to be chosen by a disambiguation process that may be based on the finer features of the language. We study the structure of these forests with respect to optimality of sharing, and in relation with the parsing schema used to produce them. In addition to a theoretical and experimental framework for studying these issues, the main results presented are: sophistication in chart parsing schemata (e.g.

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  • We present a web service for natural language parsing, prediction, generation, and translation using grammars in Portable Grammar Format (PGF), the target format of the Grammatical Framework (GF) grammar compiler. The web service implementation is open source, works with any PGF grammar, and with any web server that supports FastCGI. The service exposes a simple interface which makes it possible to use it for interactive natural language web applications. We describe the functionality and interface of the web service, and demonstrate several applications built on top of it. ...

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  • The Constituent Likelihood Automatic Word-tagging System (CLAWS) was originally designed for the low-level grammatical analysis of the million-word LOB Corpus of English text samples. CLAWS does not attempt a full parse, but uses a firat-order Markov model of language to assign word-class labels to words. CLAWS can be modified to detect grammatical errors, essentially by flagging unlikely word-class transitions in the input text.

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