Talking about Forests: an Example of Sharing Information Expressed with Vague Terms

Most natural language terms do not have precise universally agreed definitions that fix their meanings. Even when conversation participants share the same vocabulary and agree on taxonomic relationships (such as subsumption and mutual exclusivity, which might be encoded in an ontology), they may differ greatly in the specific semantics they give to the terms. We illustrate this with the example of `forest', for which the problematic arising of the assignation of different meanings is repeatedly reported in the literature. This is especially the case in the context of an unprecedented scale of publicly available geographic data, where information and databases, even when tagged to ontologies, may present a substantial semantic variation, which challenges interoperability and knowledge exchange. Our research addresses the issue of conceptual vagueness in ontology by providing a framework based on supervaluation semantics that explicitly represents the semantic variability of a concept as a set of admissible precise interpretations. Moreover, we describe the tools that support the conceptual negotiation between an agent and the system, and the specification and reasoning within standpoints

Automatic Generation of Typicality Measures for Spatial Language in Grounded Settings

In cognitive accounts of concept learning and representation three modelling approaches provide methods for assessing typicality: rulebased, prototype and exemplar models. The prototype and exemplar models both rely on calculating a weighted semantic distance to some central instance or instances. However, it is not often discussed how the central instance(s) or weights should be determined in practice. In this paper we explore how to automatically generate prototypes and typicality measures of concepts from data, introducing a prototype model and discussing and testing against various cognitive models. Following a previous pilot study, we build on the data collection methodology and have conducted a new experiment which provides a case study of spatial language for the current proposal. After providing a brief overview of cognitive accounts and computational models of spatial language, we introduce our data collection environment and study. Following this, we then introduce various models of typicality as well as our prototype model, before comparing them using the collected data and discussing the results. We conclude that our model provides significant improvement over the other given models and also discuss the improvements given by a novel inclusion of functional features in our model

Investigating the Dimensions of Spatial Language

Spatial prepositions in the English language can be used to denote a vast array of configurations which greatly diverge from any typical meaning and there is much discussion regarding how their semantics are shaped and understood. Though there is general agreement that non-geometric aspects play a significant role in spatial preposition usage, there is a lack of available data providing insight into how these extra semantic aspects should be modelled. This paper is aimed at facilitating the acquisition of data that supports theoretical analysis and helps understand the extent to which different kinds of features play a role in the semantics of spatial prepositions. We first consider key features of spatial prepositions given in the literature. We then introduce a framework intended to facilitate the collection of rich data; including geometric, functional and conventional features. Finally, we describe a preliminary study, concluding with some insights into the difficulties of modelling spatial prepositions and gathering meaningful data about them

Identifying and modelling polysemous senses of spatial prepositions in referring expressions

In this paper we analyse the issue of reference using spatial language and examine how the polysemy exhibited by spatial prepositions can be incorporated into semantic models for situated dialogue. After providing a brief overview of polysemy in spatial language and a review of related work, we describe an experimental study we used to collect data on a set of relevant spatial prepositions. We then establish a semantic model in which to integrate polysemy (the Baseline Prototype Model), which we test against a Simple Relation Model and a Perceptron Model. To incorporate polysemy into the baseline model we introduce two methods of identifying polysemes in grounded settings. The first is based on ‘ideal meanings’ and a modification of the ‘principled polysemy’ framework and the second is based on ‘object-specific features’. In order to compare polysemes and aid typicality judgements we then introduce a notion of ‘polyseme hierarchy’. Finally, we test the performance of the polysemy models against the Baseline Prototype Model and Perceptron Model and discuss the improvements shown by the polysemy models

Categorisation, Typicality & Object-Specific Features in Spatial Referring Expressions

Various accounts of cognition and semantic representations have highlighted that, for some concepts, different factors may influence category and typicality judgements. In particular, some features may be more salient in categorisation tasks while other features are more salient when assessing typicality. In this paper we explore the extent to which this is the case for English spatial prepositions and discuss the implications for pragmatic strategies and semantic models. We hypothesise that object-specific features — related to object properties and affordances — are more salient in categorisation, while geometric and physical relationships between objects are more salient in typicality judgements. In order to test this hypothesis we conducted a study using virtual environments to collect both category and typicality judgements in 3D scenes. Based on the collected data we cannot verify the hypothesis and conclude that object-specific features appear to be salient in both category and typicality judgements, further evidencing the need to include these types of features in semantic models

Modelling the Polysemy of Spatial Prepositions in Referring Expressions

In previous work exploring how to automatically generate typicality measures for spatial prepositions in grounded settings, we considered a semantic model based on Prototype Theory and introduced a method for learning its parameters from data. However, though there is much to suggest that spatial prepositions exhibit polysemy, each term was treated as exhibiting a single sense. The ability for terms to represent distinct but related meanings is unexplored in the work on grounded semantics and referring expressions, where even homonymy is rarely considered. In this paper we address this problem by analysing the issue of reference using spatial language and examining how the polysemy exhibited by spatial prepositions can be incorporated into semantic models for situated dialogue. We support our approach on theoretical developments of Prototype Theory, which suggest that polysemy may be analysed in terms of radial categories, characterised by having several prototypicality centres. After providing a brief overview of polysemy in spatial language and a review of the related work, we define the Baseline Model and discuss how polysemy may be incorporated to improve it. We introduce a method of identifying polysemes based on `ideal meanings' and a modification of the `principled polysemy' framework. In order to compare polysemes and aid typicality judgements we then introduce a notion of `polyseme hierarchy'. Subsequently, we test the performance of the extended Polysemy Model by comparing it to the Baseline Model as well as a data-driven model of polysemy which we derive with a clustering algorithm. We conclude that our method for incorporating polysemy into the Baseline Model provides significant improvement. Finally, we analyse the properties and behaviour of the generated Polysemy Model, providing some insight into the improvement in performance, as well as justification for the given methods