In the field of Artificial Intelligence the task of spatial language understanding is a particularly complex one. Textual spatial information is frequently represented by so-called locative expressions, incorporating spatial prepositions. However, apart from the spatial domain, these prepositions can occur in a wide range of senses (e.g., temporal, manner, cause, instrument) as well as in semantically transformed senses (e.g., metaphors and metonymies). Existing practical approaches usually disregard semantic transformations or falsely classify them as spatial, although they represent the majority of cases. For the efficient extraction of locative expressions from data streams (e.g. from social media sources), a fast filter mechanism for this non-spatial information is needed. Hence, we present a classification schema to quickly and robustly disambiguate spatial from non-spatial uses of prepositions. We conduct an inter-annotator agreement test to highlight the feasibility and comprehensibility of our schema based on examples sourced from a large social media corpus. We further identify the most promising existing natural language processing tools in order to combine machine learning features with fixed rules.
['André Dittrich', 'Maria Vasardani', 'Stephan Winter', 'Timothy Baldwin', 'Fei Liu']
A Classification Schema for Fast Disambiguation of Spatial Prepositions