Herbert Clark argues that language use is more than the sum of a speaker speaking and a listener listening. It is the joint action that emerges when speakers and listeners, writers and readers perform their individual actions in coordination, as ensembles. In contrast to work within the cognitive sciences, which has seen language use as an individual process, and to work within the social sciences, which has seen it as a social process, the author argues strongly that language use embodies (...) both individual and social processes. (shrink)

Les phénomènes de saillance mettent en avant un élément d’un message linguistique ou visuel. En confrontant des travaux issus de disciplines variées, nous proposons une classification des facteurs qui déterminent la saillance d’une entité dans un énoncé linguistique ou dans une scène visuelle. Certains de ces facteurs ne dépendent que des caractéristiques physiques du message : on parlera alors de saillance physique. D’autres facteurs dépendent des processus cognitifs du sujet traitant le message : on parlera alors de saillance cognitive. A (...) partir de cette classification, nous montrons que les notions de P-saillance et de C-saillance ne sont pas tributaires de la modalité à laquelle on les applique. Ceci nous permet d’aborder une caractérisation générique de la saillance physique et d’en tirer des conclusions sur la notion de structure informationnelle. (shrink)

Six experiments explored the hypothesis that the members of categories which are considered most prototypical are those with most attributes in common with other members of the category and least attributes in common with other categories. In probabilistic terms, the hypothesis is that prototypicality is a function of the total cue validity of the attributes of items. In Experiments 1 and 3, subjects listed attributes for members of semantic categories which had been previously rated for degree of prototypicality. High positive (...) correlations were obtained between those ratings and the extent of distribution of an item's attributes among the other items of the category. In Experiments 2 and 4, subjects listed superordinates of category members and listed attributes of members of contrasting categories. Negative correlations were obtained between prototypicality and superordinates other than the category in question and between prototypicality and an item's possession of attributes possessed by members of contrasting categories. Experiments 5 and 6 used artificial categories and showed that family resemblance within categories and lack of overlap of elements with contrasting categories were correlated with ease of learning, reaction time in identifying an item after learning, and rating of prototypicality of an item. It is argued that family resemblance offers an alternative to criterial features in defining categories. (shrink)

We examine the problem of generating definite noun phrases that are appropriate referring expressions; that is, noun phrases that (a) successfully identify the intended referent to the hearer whilst (b) not conveying to him or her any false conversational implicatures (Grice, 1975). We review several possible computational interpretations of the conversational implicature maxims, with different computational costs, and argue that the simplest may be the best, because it seems to be closest to what human speakers do. We describe our recommended (...) algorithm in detail, along with a specification of the resources a host system must provide in order to make use of the algorithm, and an implementation used in the natural language generation component of the IDAS system. (shrink)

This study investigates to what extent the amount of variation in a visual scene causes speakers to mention the attribute color in their definite target descriptions, focusing on scenes in which this attribute is not needed for identification of the target. The results of our three experiments show that speakers are more likely to redundantly include a color attribute when the scene variation is high as compared with when this variation is low (even if this leads to overspecified descriptions). We (...) argue that these findings are problematic for existing algorithms that aim to automatically generate psychologically realistic target descriptions, such as the Incremental Algorithm, as these algorithms make use of a fixed preference order per domain and do not take visual scene variation into account. (shrink)

We report two experiments that investigated the widely held assumption that speakers use the addressee’s discourse model when choosing referring expressions (e.g., Ariel, 1990; Chafe, 1994; Givón, 1983; Prince, 1985), by manipulating whether the addressee could hear the immediately preceding linguistic context. Experiment 1 showed that speakers increased pronoun use (and decreased noun phrase use) when the referent was mentioned in the immediately preceding sentence compared to when it was not, even though the addressee did not hear the preceding sentence, (...) indicating that speakers used their own, privileged discourse model when choosing referring expressions. The same pattern of results was found in Experiment 2. Speakers produced more pronouns when the immediately preceding sentence mentioned the referent than when it mentioned a referential competitor, regardless of whether the sentence was shared with their addressee. Thus, we conclude that choice of referring expression is determined by the referent’s accessibility in the speaker’s own discourse model rather than the addressee’s. (shrink)

This article offers a survey of computational research on referring expressions generation (REG). It introduces the REG problem and describes early work in this area, discussing what basic assumptions lie behind it, and showing how its remit has widened in recent years. We discuss computational frameworks underlying REG, and demonstrate a recent trend that seeks to link up REG algorithms with well-established Knowledge Representation traditions. Considerable attention is given to recent efforts at evaluating REG algorithms and the lessons that they (...) allow us to learn. The article concludes with a discussion of the way forward in REG, focussing on references in larger and more realistic settings. (shrink)

Most everyday tasks involve multiple modalities, which raises the question of how the processing of these modalities is coordinated by the cognitive system. In this paper, we focus on the coordination of visual attention and linguistic processing during speaking. Previous research has shown that objects in a visual scene are fixated before they are mentioned, leading us to hypothesize that the scan pattern of a participant can be used to predict what he or she will say. We test this hypothesis (...) using a data set of cued scene descriptions of photo-realistic scenes. We demonstrate that similar scan patterns are correlated with similar sentences, within and between visual scenes; and that this correlation holds for three phases of the language production process (target identification, sentence planning, and speaking). We also present a simple algorithm that uses scan patterns to accurately predict associated sentences by utilizing similarity-based retrieval. (shrink)

A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns out that (...) the success of the IA depends substantially on the ‘‘preference order’’ (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO. (shrink)