The background for this paper is the aim to build robotic assistants that can “naturally” interact with humans. One prerequisite for this is that the robot can correctly identify objects or places a user refers to, and produce comprehensible references itself. As robots typically act in environments that are larger than what is immediately perceivable, the problem arises how to identify the appropriate context, against which to resolve or produce a referring expression (RE). Existing algorithms for generating REs generally bypass this problem by assuming a given context. In this paper, we explicitly address this problem, proposing a method for context determination in large-scale space. We show how it can be applied both for resolving and producing REs. 1

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 considerably 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 what we see as the way forward in REG, focussing on references in larger and more realistic settings. 1.

The generation of referring expressions (GRE), an important subtask of Natural Language Generation (NLG) is to generate phrases that uniquely identify domain entities. Until recently, many GRE algorithms were developed using only simple formalisms, which were taylor made for the task. Following the fast development of ontology-based systems, reinterpretations of GRE in terms of description logic (DL) have recently started to be studied. However, the expressive power of these DL-based algorithms is still limited, not exceeding that of older GRE approaches.

Abstract This paper presents a Conceptual Graph (cg) framework to

In this paper we present an approach to the task of generating and resolving referring expressions (REs) for conversational mobile robots. It is based on a spatial knowledge base encompassing both robot- and human-centric representations. Existing algorithms for the generation of referring expressions (GRE) try to find a description that uniquely identifies the referent with respect to other entities that are in the current context. Mobile robots, however, act in large-scale space, that is environments that are larger than what can be perceived at a glance, e.g. an office building with different floors, each containing several rooms and objects. One challenge when referring to elsewhere is thus to include enough information so that the interlocutors can extend their context appropriately. We address this challenge with a method for context construction that can be used for both generating and resolving REs – two previously disjoint aspects. Our approach is embedded in a bi-directional framework for natural language processing for robots. 1

Many studies in natural language processing are concerned with how to generate definite descriptions that evoke a discourse entity already introduced in the context. A solution to this problem has been initially proposed by Dale (1989) in terms of distinguishing descriptions and distinguishable entities. In this paper, we give a formal definition of the terms “distinguishable entity ” in non trivial cases and we show that its properties lead us to the definition of a distance between entities. Then, we give a polynomial algorithm to compute distinguishing descriptions. 1

Abstract. The task of generating referring expressions, an important subtask of Natural Language Generation is to generate phrases that uniquely identify domain entities. Until recently, many GRE algorithms were developed using only simple and essentially home-made formalisms. Following the fast development of ontology-based systems, reinterpretations of GRE in terms of description logic have been studied. However, the quantifiers generated are still limited, not exceeding the works covered by existing GRE approaches. In this paper, we propose an DL-based approach to GRE that exploits the full power of OWL2 to generate referring expressions that goes beyond the expressivity of previous GRE algorithms. The potential of DL reasoning in GRE is also discussed. 1 GRE and KR: the story so far Generation of Referring Expressions (GRE) is the subtask of Natural Language Generation (NLG) that focuses on the identification of objects in natural language. For example, Fig.1 depicts the relations between several individuals of women, dogs and cats. In such a scenario, a GRE system might identify a given object as “Dog ” or, if this fails to

A key task of almost any Natural Language Generation (NLG) system is to refer to an entity. Linguists and philosophers have a long tradition of theorising about reference. In the words of the philosopher John Searle, Any expression which serves to identify any thing, process, event, action, or any