Constraints on the generation

Xem 1-20 trên 45 kết quả Constraints on the generation
  • This paper presents an analysis of a family of particular English constructions, all of which roughly express "purpose". In particular we look at the purpose clause, rationale .clause, and infinitival relative clause.

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  • This paper describes the NECA MNLG; a fully implemented Multimodal Natural Language Generation module. The MNLG is deployed as part of the NECA system which generates dialogues between animated agents. The generation module supports the seamless integration of full grammar rules, templates and canned text. The generator takes input which allows for the specification of syntactic, semantic and pragmatic constraints on the output.

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  • This paper presents a cooperative consultation system on a restricted domain. The system builds hypotheses on the user's plan and avoids misunderstandings (with consequent repair dialogues) through clarification dialogues in case of ambiguity. The role played by constraints in the generation of the answer is characterized in order to limit the cases of ambiguities requiring a clarification dialogue. The answers of the system are generated at different levels of detail, according to the user's competence in the domain. ...

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  • Recent developments in generation algorithms have enabled work in nnificafion-based computational linguistics to approach more closely the ideal of grammars as declarative statements of linguistic facts, neutral between analysis an0_synthesis, x-"~-oui this perspective, however, the situation is still far from perfect; all known methods of generation impose constraints on the grammars they assume.

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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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  • 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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  • This paper introduces primitive Optimality Theory (OTP), a linguistically motivated formalization of OT. OTP specifies the class of autosegmental representations, the universal generator Gen, and the two simple families of permissible constraints. In contrast to less restricted theories using Generalized Alignment, OTP's optimal surface forms can be generated with finite-state methods adapted from (Ellison, 1994). Unfortunately these methods take time exponential on the size of the grammar. Indeed the generation problem is shown NP-complete in this sense. ...

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  • While the generative view of language processing builds bigger units out of smaller ones by means of rewriting steps, the axiomatic view eliminates invalid linguistic structures out of a set of possible structures by means of wellformedness principles. We present a generator based on the axiomatic view and argue that when combined with a TAG-like grammar and a flat semantics, this axiomatic view permits avoiding drawbacks known to hold either of top-down or of bottom-up generators.

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  • Computer-based generation of natural language requires consideration of two different types of problems: i) determining the content and textual shape of what is to be said, and 2) transforming that message into English. A computational solution to the problems of deciding what to say and how to organize it effectively is proposed that relies on an interaction between structural and semantic processes. Schemas, which encode aspects of discourse structure, are used to guide the generation process.

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  • We present a new grammatical formalism called Constraint Dependency G r a m m a r (CDG) in which every grammatical rule is given as a constraint on wordto-word modifications. CDG parsing is formalized as a constraint satisfaction problem over a finite domain so that efficient constraint-propagation algorithms can be employed to reduce structural ambiguity without generating individual parse trees. The weak generative capacity and the computational complexity of CDG parsing are also discussed.

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  • The paper proposes an equivalence relation using the closure of itemset to partition the solution set into disjoint equivalence classes and a new, efficient representation of the rules in each class based on the lattice of closed itemsets and their generators. The paper also develops a new algorithm, called MAR-MINSC, to rapidly mine all constrained rules from the lattice instead of mining them directly from the database.

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  • We propose a new specifically designed method for paraphrase generation based on Monte-Carlo sampling and show how this algorithm is suitable for its task. Moreover, the basic algorithm presented here leaves a lot of opportunities for future improvement. In particular, our algorithm does not constraint the scoring function in opposite to Viterbi based decoders. It is now possible to use some global features in paraphrase scoring functions. This algorithm opens new outlooks for paraphrase generation and other natural language processing applications like statistical machine translation.

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  • We show that the main testable implication of the buffer stock model is that the covariance between the wealth gap (the difference between actual and target wealth) and consumption is (strongly) positive. Although we focus on Carroll’s version of the buffer stock model, the test applies equally well to Deaton’s case. In Carroll, buffer stock behavior emerges from the tension between impatience, prudence, and the chance of zero earnings. Impatient individuals would like to anticipate consumption, but the chance of zero future earnings generates a demand for wealth.

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  • In this paper, we propose an interpretation of these events in which the prospect of a New Economy plays a key role in generating the other events. More specifically, we show that the mere prospect of high future productiv- ity growth can generate sizable gains in current productivity, as well as an economic expansion, a stock market boom and a financing boom for new firms. There are two main ingredients to our story: financing constraints due to limited contract enforceability and firm-level diminishing returns to scale.

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  • This paper proposes a method for generating a logicalconstraint-based internal representation from a unification grammar formalism with disjunctive information. Unification grammar formalisms based on path equations and lists of pairs of labels and values are better than those based on first-order terms in that the former is easier to describe and to understand. Parsing with term-based internal representations is more efficient than parsing with graph-based representations.

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  • Natural-Language Generation from flat semantics is an NP-complete problem. This makes it necessary to develop algorithms that run with reasonable efficiency in practice despite the high worstcase complexity. We show how to convert TAG generation problems into dependency parsing problems, which is useful because optimizations in recent dependency parsers based on constraint programming tackle exactly the combinatorics that make generation hard. Indeed, initial experiments display promising runtimes. ...

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  • This work is concerned with the development of instruments for GB parsing. An alternative to the well known indexation system of (Chomsky, 1981) will be proposed and then used to formalize the view of Binding Theory in terms of the generation of constraints on the referential properties of the NPs of a sentence. Finally the problems of verification and satisfiability of BT will be addressed within the proposed framework.

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  • This paper describes a novel instancebased sentence boundary determination method for natural language generation that optimizes a set of criteria based on examples in a corpus. Compared to existing sentence boundary determination approaches, our work offers three significant contributions. First, our approach provides a general domain independent framework that effectively addresses sentence boundary determination by balancing a comprehensive set of sentence complexity and quality related constraints.

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  • In a language generation system, a content planner embodies one or more “plans” that are usually hand–crafted, sometimes through manual analysis of target text. In this paper, we present a system that we developed to automatically learn elements of a plan and the ordering constraints among them. As training data, we use semantically annotated transcripts of domain experts performing the task our system is designed to mimic.

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  • The incremental algorithm introduced in (Dale and Reiter, 1995) for producing distinguishing descriptions does not always generate a minimal description. In this paper, I show that when generalised to sets of individuals and disjunctive properties, this approach might generate unnecessarily long and ambiguous and/or epistemically redundant descriptions.

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