Referring expression generation

Xem 1-20 trên 24 kết quả Referring expression generation
  • Current Referring Expression Generation algorithms rely on domain dependent preferences for both content selection and linguistic realization. We present two experiments showing that human speakers may opt for dispreferred properties and dispreferred modifier orderings when these were salient in a preceding interaction (without speakers being consciously aware of this). We discuss the impact of these findings for current generation algorithms.

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  • We present a data-driven approach to learn user-adaptive referring expression generation (REG) policies for spoken dialogue systems. Referring expressions can be difficult to understand in technical domains where users may not know the technical ‘jargon’ names of the domain entities. In such cases, dialogue systems must be able to model the user’s (lexical) domain knowledge and use appropriate referring expressions.

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  • In this paper we present research in which we apply (i) the kind of intrinsic evaluation metrics that are characteristic of current comparative HLT evaluation, and (ii) extrinsic, human task-performance evaluations more in keeping with NLG traditions, to 15 systems implementing a language generation task. We analyse the evaluation results and find that there are no significant correlations between intrinsic and extrinsic evaluation measures for this task.

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  • This paper focuses on generating referring expressions capable of serving multiple communicative goals. The components of a referring expression are divided into a referring part and a non-referring part. Two rules for the content determination and construction of the non-referring part are given, which are realised in an embedding algorithm.

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  • Generating referring expressions is a key step in Natural Language Generation. Researchers have focused almost exclusively on generating distinctive referring expressions, that is, referring expressions that uniquely identify their intended referent. While undoubtedly one of their most important functions, referring expressions can be more than distinctive. In particular, descriptive referring expressions – those that provide additional information not required for distinction – are critical to fluent, efficient, well-written text. ...

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  • One of the challenges in the automatic generation of referring expressions is to identify a set of domain entities coherently, that is, from the same conceptual perspective. We describe and evaluate an algorithm that generates a conceptually coherent description of a target set. The design of the algorithm is motivated by the results of psycholinguistic experiments.

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  • Previous algorithms for the generation of referring expressions have been developed specifically for this purpose. Here we introduce an alternative approach based on a fully generic aggregation method also motivated for other generation tasks. We argue that the alternative contributes to a more integrated and uniform approach to content determination in the context of complete noun phrase generation.

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  • A simple formalism is proposed to represent the contexts in which pronouns, definite/indefinite descriptions, and ordinal descriptions (e.g. 'the second book') can be used, and the way in which these expressions change the context. It is shown that referring expressions can be generated by a unification grammar provided that some phrase-structure rules are specially tailored to express entities in the current knowledge base.

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  • We present an algorithm for generating referring expressions in open domains. Existing algorithms work at the semantic level and assume the availability of a classification for attributes, which is only feasible for restricted domains. Our alternative works at the realisation level, relies on WordNet synonym and antonym sets, and gives equivalent results on the examples cited in the literature and improved results for examples that prior approaches cannot handle.

    pdf8p bunbo_1 17-04-2013 26 1   Download

  • This paper presents a computational model of how conversational participants collaborate in making referring expressions. The model is based on the planning paradigm. It employs plans for constructing and recognizing referring expressions and meta-plans for constructing and recognizing clarifications. This allows the model to account for the generation and understanding both of referring expressions and of their clarifications in a uniform framework using a single knowledge base.

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  • This paper presents an approach to incrementally generating locative expressions. It addresses the issue of combinatorial explosion inherent in the construction of relational context models by: (a) contextually defining the set of objects in the context that may function as a landmark, and (b) sequencing the order in which spatial relations are considered using a cognitively motivated hierarchy of relations, and visual and discourse salience.

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  • We translate sentence generation from TAG grammars with semantic and pragmatic information into a planning problem by encoding the contribution of each word declaratively and explicitly. This allows us to exploit the performance of off-the-shelf planners. It also opens up new perspectives on referring expression generation and the relationship between language and action.

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  • This paper describes the referring expression generation mechanisms used in EPICURE, a computer program which produces natural language descriptions of cookery recipes. Major features of the system include: an underlying ontology which permits the representation of non-singular entities; a notion of diacriminatory power, to determine what properties should be used in a description; and a PATR-like unification grammar to produce surface linguistic strings.

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  • Existing algorithms for generating referential descriptions to sets of objects have serious deficits: while incremental approaches may produce ambiguous and redundant expressions, exhaustive searches are computationally expensive. Mediating between these extreme control regimes, we propose a best-first searching algorithm for uniquely identifying sets of objects. We incorporate linguistically motivated preferences and several techniques to cut down the search space. Preliminary results show the effectiveness of the new algorithm. ...

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  • The core of the problem is finding a way of describing the intended referent that distinguishes it from other potential referents with which it might be confused. We refer to this problem as the c o n t e n t d e t e r m i n a t i o n task. In this paper, we point out some limitations in an earlier solution proposed in Dale [1988, 1989], and discuss the possibilites of extending this solution by incorporating a use of constraints motivated by the work of Haddock [1987, 1988].

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  • We present a natural language generation approach which models, exploits, and manipulates the non-linguistic context in situated communication, using techniques from AI planning. We show how to generate instructions which deliberately guide the hearer to a location that is convenient for the generation of simple referring expressions, and how to generate referring expressions with context-dependent adjectives.

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  • This paper discusses two problems that arise in the Generation of Referring Expressions: (a) numeric-valued attributes, such as size or location; (b) perspective-taking in reference. Both problems, it is argued, can be resolved if some structure is imposed on the available knowledge prior to content determination. We describe a clustering algorithm which is sufficiently general to be applied to these diverse problems, discuss its application, and evaluate its performance. close’ on the given dimension, and ‘sufficiently distant’ from those of their distractors. ...

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  • Pipelined Natural Language Generation (NLG) systems have grown increasingly complex as architectural modules were added to support language functionalities such as referring expressions, lexical choice, and revision. This has given rise to discussions about the relative placement of these new modules in the overall architecture. Recent work on another aspect of multi-paragraph text, discourse markers, indicates it is time to consider where a discourse marker insertion algorithm fits in.

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  • In this paper we investigate how much data is required to train an algorithm for attribute selection, a subtask of Referring Expressions Generation (REG). To enable comparison between different-sized training sets, a systematic training method was developed. The results show that depending on the complexity of the domain, training on 10 to 20 items may already lead to a good performance.

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  • This study employs a knowledge intensive corpus analysis to identify the elements of the communicative context which can be used to determine the appropriate lexical and grammatical form of instructional texts. IMAGENE, an instructional text generation system based on this analysis: is presented, particularly with reference to its expression of precondition relations. INTRODUCTION Technical writers routinely employ a range of forms of expression for precondition expressions in instructional text.

    pdf8p bunmoc_1 20-04-2013 34 1   Download



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