When people interact with language-producing agents (other people or computers), they assume that the shared experience leads to shared representations — of the world, the interaction, and the language used in the interaction. This phenomenon occurs even during interaction with systems that give no evidence of building shared representations. The absence of shared representations leads to errors and delays; alternatively, even simple shared representations can lead to reduced error rates and more efficient interaction. In this talk, we present three case studies: a mobile local business search application that builds no interaction representations; a telephone-based recommendation and review system that builds limited representations of the shared language in the interaction; and computer models of coreference that use shared representations to permit both coreference resolution and referring expression generation. We lay out a range of possibilities for shared representations, show that they can be built incrementally as an interaction progresses, and point to possibilities for future work in probabilistic shared representations for interactive systems.

Negation is present in all human languages and it is used to reverse the polarity of parts of a statement. It is a complex phenomenon that interacts with many other aspects of language. Besides the direct meaning, negated statements often carry a latent positive meaning. Negation can be interpreted in terms of its scope and focus. This paper explores the importance of both scope and focus to capture the meaning of negated statements. Some issues on detecting negation from text are outlined, the forms in which negation occurs are depicted and heuristics to detect its scope and focus are proposed.

In informational terms, a module dedicated to process information always has specific inputs and outputs. It describes a particular process constrained by specific rules. A processing chain can be a serial combination or a parallel combination of such modules. Thus, an architecture of language engineering, each processing chain becomes a particular instantiation of all possible paths. A processing chain is built from a choice of tasks underlying modules that an engineer wants to apply to the text. Therefore, in this perspective, a fundamental question arises: given a set of modules, what are the eligible chains of all combinations of the given modules? This is what we will discuss about in our paper.

Could we enrich speech-act theory to deal with discourse? Wittgenstein and Searle are sceptical. In my view, the primary aim of discourse pragmatics is to analyze the structure and dynamics of language-games with an internal conversational goal. Logic can analyze felicity-conditions of such collective illocutions. For interlocutors obey systems of constitutive rules in conducting descriptive, deliberative, declaratory or expressive dialogues. I will show how to construct speaker-meaning from sentence-meaning, conversational background and maxims. I will also explain how to use the resources of formalisms and mathematical logic and to further develop intensional and illocutionary logics, the logic of attitudes and of action in order to characterize our ability to converse. I will also deal with the nature of intelligent dialogues between man and machines in A.I.

In this paper, we discuss the question of closure conditions for dialogues in three different frameworks: W. C. Mann's DMT framework, Vanderveken's illocutionary theory of discourse and Asher and Lascarides SDRT approach. We are interested in formal frameworks that aim to describe the logical structure of conversations between diversely bounded agents who are — to some extent — rational, intelligent, linguistically competent and who possess some awareness of their environment and some knowledge of the circumstances of their interactions. We use the notion of closure conditions as a benchmark for theory comparison.

Linguistic and semantic annotations are important features for text-based applications. However, achieving and maintaining a good quality of a set of annotations is known to be a complex task. Many ad hoc approaches have been developed to produce various types of annotations, while comparing those annotations to improve their quality is still rare. In this paper, we propose a framework in which both linguistic and domain information can cooperate to reason with annotations. The underlying knowledge representation issues are carefully analyzed and solved by studying a higher order logic, which accounts for the cooperation of different sorts of knowledge. Our prototype implements this logic based on a reduction to classical description logics by preserving the semantics, allowing us to benefit from cutting-edge Semantic Web reasoners. An application scenario shows interesting merits of this framework on reasoning with annotations of texts.

In this paper, we present an image and text mining tool named TNT. This tool is based on Contextual Exploration and work on different points of view. It can process a corpus of all sizes in French or in English. The web interface associated with this tool, offers a reorganization of the text guided by the images and annotated segments that are associated.

In computer science, ontology is a model of a domain in the form of classes and of relationships between these classes. Classes are organized in a graph the arrows of which are semantic relations. Ontology is static because the class hierarchy is fixed. In paleontology, systematic (i.e., the class hierarchies and the class relationships) is complicated by the time variable. Morphological changes over time yield, by natural selection, the emergence of new forms (taxa) differing from the ancestral morph and contemporaneous taxa of the same class hierarchy. Discovering new taxa implies, therefore, the rearrangement of the class hierarchy or the definition of new classes, based on the degree of atypicality of the new morph. Note that this phenomenon occurs in many domains such as physics, biology, linguistics, for example.

We define sentence generalization and generalization diagrams as a special case of least general generalization (LGG) as applied to linguistic parse trees. Similarity measure between linguistic parse trees is developed as LGG operation on the lists of sub-trees of these trees. The diagrams introduced are representation of mapping between the syntactic generalization level and semantic generalization level. Generalization diagrams are intended as a framework to compute semantic similarity between texts relying on linguistic parse tree data. Such structured approach significantly improves text relevance assessment in a horizontal domain, where ontologies are not available

We introduce a search engine and information retrieval system for providing access to opinion data. Natural language technology of generalization of syntactic parse trees is introduced as a similarity measure between subjects of textual opinions to link them on the fly. Information extraction algorithm for automatic summarization of web pages in the format of Google sponsored links is presented. We outline the usability of the implemented system, integrated opinion delivery environment (IODE).