Analysing the Usage of Spatial Prepositions in Short Messages

Dittrich, André; Richter, Daniela; Lucas, Christian

doi:10.1007/978-3-319-11879-6_11

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…Indeed, most researchers only have access to some small proportion of the total volume of Tweets. Twitter messages are short, often with a relatively simple language structure (Dittrich, Richter, and Lucas 2015), covering a wide variety of topics without a focus on specific domain (Go, Bhayani, and Huang 2009;Kwak et al 2010) and are a popular source for research, despite a wide range of challenges including a high frequency of misspelling and slang (Go, Bhayani, and Huang 2009), the use and mixing of multiple languages (Hong, Convertino, and Chi 2011), the prevalence (especially, it appears, in geocoded Tweets) of bots (Chu et al 2010;Compton, Jurgens, and Allen 2014) and the fundamental question of whether or not location is strongly correlated with the topic of discussion in a Tweet (Hahmann, Purves, and Burghardt 2014). In terms of LBS this is of crucial importance, since, unlike images, the location of a Tweet is associated with where something was said, rather than the location of the object being described.…”

Section: Rationalementioning

confidence: 99%

Approaching location-based services from a place-based perspective: from data to services?

Bahrehdar

Chesnokova

Purves

2019

Journal of Location Based Services

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Despite the seemingly obvious importance of a link between notions of place and the provision of context in locationbased services (LBS), truly place-based LBS remain rare. Place is attractive as a concept for designing services as it focuses on ways in which people, rather than machines, represent and talk about places. We review papers which have extracted place-relevant information from a variety of sources, examining their rationales, the data sources used, the characteristics of the data under study and the ways in which place is represented. Although the data sources used are subject to a wide range of biases, we find that existing methods and data sources are capable of extracting a wide range of place-related information. We suggest categories of LBS which could profit from such information, for example, by using place-related natural language (e.g. vernacular placenames) in tracking and routing services and moving the focus from geometry to place semantics in location-based retrieval. A key future challenge will be to integrate data derived from multiple sources if we are to advance from individual case studies focusing on a single aspect of place to services which can deal with multiple aspects of place.

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Section: Rationalementioning

confidence: 99%

Approaching location-based services from a place-based perspective: from data to services?

Bahrehdar

Chesnokova

Purves

2019

Journal of Location Based Services

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“…Hence, we argue that for this work an existence of a core meaning is not essential. Dittrich et al [8] showed that the claimed core meaning, i.e. the spatial meaning, is not necessarily the most frequent one.…”

Section: Fundamental Literature On Spatial Prepositionsmentioning

confidence: 99%

“…In this research, we study an extensive list of English prepositions that are typically considered to be potentially spatial. The source of this list is Dittrich et al [8], who investigated more than sixty prepositions in the domain of short message services (Twitter). They compiled a comprehensive summary including a statistical probability for each preposition to encode a spatial relation based on their corpus.…”

Section: Choice Of Prepositionsmentioning

confidence: 99%

See 1 more Smart Citation

A Classification Schema for Fast Disambiguation of Spatial Prepositions

Dittrich

Vasardani

Winter

et al. 2015

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming

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In the field of Artificial Intelligence the task of spatial language understanding is a particularly complex one. Textual spatial information is frequently represented by socalled 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 nonspatial information is needed. Hence, we present a classification schema to quickly and robustly disambiguate spatial from non-spatial uses of prepositions. We conduct an interannotator 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.

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Analysing the Usage of Spatial Prepositions in Short Messages

Cited by 2 publications

References 12 publications

Approaching location-based services from a place-based perspective: from data to services?

Approaching location-based services from a place-based perspective: from data to services?

A Classification Schema for Fast Disambiguation of Spatial Prepositions

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