Spatial role labeling annotation scheme

Given the large body of the past research on various aspects of spatial information, the main obstacles for employing machine learning for extraction of this type of information from natural language have been: a) the lack of an agreement on a unique semantic model for spatial information; b) the diversity of formal spatial representation models ; c) the gap between the expressiveness of natural language and formal spatial representation models and consequently; d) the lack of annotated data on which machine learning can be employed to learn and extract the spatial relations.In this chapter we introduce a spatial annotation scheme for natural languagethat supports various aspects of spatial semantics, including static and dynamic spatial relations. The annotation scheme is based on the ideas of holistic spatial semantics as well as qualitative spatial reasoning models. Spatial roles, their relations and indicators along with their multiple formal meanings are tagged using the annotation scheme producing a spatial language corpus. The goal of building such a corpus is to produce a resource for training the machine learning methods for mapping the language to formal spatial representation models, and to use it as ground-truth data for evaluation. We describe the foundations and the motivations for the concepts used in designing the proposed spatial annotation scheme in Section 2. We illustrate the scheme and its XML and relational representation by means of examples in Section 3. The investigated corpora, annotated data and the annotation challenges are described in Section 4. A review on the related works is provided in Section 5. We conclude in Section 6.

ISBN:978-94-024-0879-9

Publication year:2017