Constraints

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  • In this activity, you will participate in a class discussion about the implications of the constraints and requirements in the Ferguson and Bardell, Inc. case study. Microsoft Official Curriculum (MOC), available to IT Academies at a discounted price, is professional courseware intended for IT professionals and developers who build, support, and implement solutions by using Microsoft products and technologies. MOC is designed to cover the topics that employers know are mission-critical in the real world.

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  • [ Team LiB ] Recipe 6.8 Creating Constraints, PrimaryKeys, Relationships Based on Multiple Columns Problem You need to create a constraint, primary key, or a relationship between two tables in a DataSet using more than one column. Solution Use the System.Data.

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  • [ Team LiB ] Recipe 6.9 Retrieving Constraints from a SQL Server Database Problem You need to programmatically define constraints in a DataSet and retrieve constraint information defined in a SQL Server database. Solution Use the INFORMATION_SCHEMA views and SQL Server system tables to get information about primary keys, foreign keys, and check constraints.

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  • The process of finding an optimum outcome from a set of constrained resources, where the objective function and the constraints can be expressed as linear equations. The process of finding an optimum outcome from a set of constrained resources, where the objective function and the constraints can be expressed as linear equations.

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  • Name a constraint or the Oracle server generates a name by using the SYS_Cn format. Create a constraint either: At the same time as the table is created, or After the table has been created Define a constraint at the column or table level. View a constraint in the data dictionary.

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  • Source language parse trees offer very useful but imperfect reordering constraints for statistical machine translation. A lot of effort has been made for soft applications of syntactic constraints. We alternatively propose the selective use of syntactic constraints. A classifier is built automatically to decide whether a node in the parse trees should be used as a reordering constraint or not. Using this information yields a 0.8 BLEU point improvement over a full constraint-based system.

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  • This work models Word Sense Disambiguation (WSD) problem as a Distributed Constraint Optimization Problem (DCOP). To model WSD as a DCOP, we view information from various knowledge sources as constraints. DCOP algorithms have the remarkable property to jointly maximize over a wide range of utility functions associated with these constraints. We show how utility functions can be designed for various knowledge sources.

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  • Statistical systems with high accuracy are very useful in real-world applications. If these systems can capture basic linguistic information, then the usefulness of these statistical systems improve a lot. This paper is an attempt at incorporating linguistic constraints in statistical dependency parsing. We consider a simple linguistic constraint that a verb should not have multiple subjects/objects as its children in the dependency tree.

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  • We present a novel approach to discovering relations and their instantiations from a collection of documents in a single domain. Our approach learns relation types by exploiting meta-constraints that characterize the general qualities of a good relation in any domain. These constraints state that instances of a single relation should exhibit regularities at multiple levels of linguistic structure, including lexicography, syntax, and document-level context.

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  • One problem with phrase-based statistical machine translation is the problem of longdistance reordering when translating between languages with different word orders, such as Japanese-English. In this paper, we propose a method of imposing reordering constraints using document-level context. As the documentlevel context, we use noun phrases which significantly occur in context documents containing source sentences.

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  • Word alignment methods can gain valuable guidance by ensuring that their alignments maintain cohesion with respect to the phrases specified by a monolingual dependency tree. However, this hard constraint can also rule out correct alignments, and its utility decreases as alignment models become more complex. We use a publicly available structured output SVM to create a max-margin syntactic aligner with a soft cohesion constraint.

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  • Dependency analysis of natural language has gained importance for its applicability to NLP tasks. Non-projective structures are common in dependency analysis, therefore we need fine-grained means of describing them, especially for the purposes of machine-learning oriented approaches like parsing. We present an evaluation on twelve languages which explores several constraints and measures on non-projective structures. We pursue an edge-based approach concentrating on properties of individual edges as opposed to properties of whole trees. ...

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  • We show that a practical translation of MRS descriptions into normal dominance constraints is feasible. We start from a recent theoretical translation and verify its assumptions on the outputs of the English Resource Grammar (ERG) on the Redwoods corpus. The main assumption of the translation— that all relevant underspecified descriptions are nets—is validated for a large majority of cases; all non-nets computed by the ERG seem to be systematically incomplete.

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  • In statistical machine translation, the generation of a translation hypothesis is computationally expensive. If arbitrary wordreorderings are permitted, the search problem is NP-hard. On the other hand, if we restrict the possible word-reorderings in an appropriate way, we obtain a polynomial-time search algorithm. In this paper, we compare two different reordering constraints, namely the ITG constraints and the IBM constraints.

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  • Minimal Recursion Semantics (MRS) is the standard formalism used in large-scale HPSG grammars to model underspecified semantics. We present the first provably efficient algorithm to enumerate the readings of MRS structures, by translating them into normal dominance constraints.

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  • Dominance constraints are logical descriptions of trees that are widely used in computational linguistics. Their general satisfiability problem is known to be NP-complete. Here we identify the natural fragment of normal dominance constraints and show that its satisfiability problem is in deterministic polynomial time.

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  • We introduce a first-order language for semantic underspecification that we call Constraint Language for Lambda-Structures (CLLS). A Astructure can be considered as a A-term up to consistent renaming of bound variables (aequality); a constraint of CLLS is an underspecified description of a A-structure. CLLS solves a capturing problem omnipresent in underspecified scope representations. CLLS features constraints for dominance, lambda binding, parallelism, and anaphoric links. Based on CLLS we present a simple, integrated, and underspecified treatment of scope, parallelism, and anaphora. ...

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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 reports a pilot study, in which Constraint G r a m m a r inspired rules were learnt using the Progol machine-learning system. Rules discarding faulty readings of ambiguously tagged words were learnt for the part of speech tags of the Stockholm-Ume£ Corpus. Several thousand disambiguation rules were induced. When tested on unseen data, 98% of the words retained the correct reading after tagging. However, there were ambiguities pending after tagging, on an average 1.13 tags per word.

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  • This paper presents the results of a s t u d y on the semantic constraints imposed on lexical choice by certain contextual indicators. We show how such indicators are computed and how correlations between them and the choice of a noun phrase description of a named entity can be automatically established using supervised learning. Based on this correlation, we have developed a technique for automatic lexical choice of descriptions of entities in text generation.

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