Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context window
Measuring the semantic similarity between words is important for natural language processing tasks. The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial semantics of implied spatial information are rarely pre...
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| Veröffentlicht in: | PloS one 2020-07, Vol.15 (7), p.e0236347-e0236347 |
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| creator | Wang, Bozhi Fei, Teng Kang, Yuhao Li, Meng Du, Qingyun Han, Meng Dong, Ning |
| description | Measuring the semantic similarity between words is important for natural language processing tasks. The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial semantics of implied spatial information are rarely preserved. Geographic information retrieval (GIR) methods have focused on this issue; however, they sometimes fail to solve the problem because the spatial and textual similarities of words are considered and calculated separately. In this paper, from the perspective of spatial context, we consider the two parts as a whole-spatial context semantics, and we propose a method that measures spatial semantic similarity using a sliding geospatial context window for geo-tagged words. The proposed method was first validated with a set of simulated data and then applied to a real-world dataset from Flickr. As a result, a spatial semantic similarity model at different scales is presented. We believe this model is a necessary supplement for traditional textual-language semantic analyses of words obtained by word-embedding technologies. This study has the potential to improve the quality of recommendation systems by considering relevant spatial context semantics, and benefits linguistic semantic research by emphasising the spatial cognition among words. |
| doi_str_mv | 10.1371/journal.pone.0236347 |
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The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial semantics of implied spatial information are rarely preserved. Geographic information retrieval (GIR) methods have focused on this issue; however, they sometimes fail to solve the problem because the spatial and textual similarities of words are considered and calculated separately. In this paper, from the perspective of spatial context, we consider the two parts as a whole-spatial context semantics, and we propose a method that measures spatial semantic similarity using a sliding geospatial context window for geo-tagged words. The proposed method was first validated with a set of simulated data and then applied to a real-world dataset from Flickr. As a result, a spatial semantic similarity model at different scales is presented. We believe this model is a necessary supplement for traditional textual-language semantic analyses of words obtained by word-embedding technologies. This study has the potential to improve the quality of recommendation systems by considering relevant spatial context semantics, and benefits linguistic semantic research by emphasising the spatial cognition among words.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0236347</identifier><identifier>PMID: 32702022</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Bars ; Biology and Life Sciences ; Cognition ; Cognition & reasoning ; Comprehension ; Computer and Information Sciences ; Computer simulation ; Context ; Earth Sciences ; Electric power ; Embedding ; Environmental science ; Humans ; Information processing ; Information retrieval ; Information Storage and Retrieval ; Keywords ; Language ; Linguistics - trends ; Medicine and Health Sciences ; Natural language ; Natural Language Processing ; PubMed ; Recommender systems ; Research and Analysis Methods ; Semantics ; Similarity ; Social Sciences ; Software ; Spatial data ; Visualization ; Words (language)</subject><ispartof>PloS one, 2020-07, Vol.15 (7), p.e0236347-e0236347</ispartof><rights>2020 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial semantics of implied spatial information are rarely preserved. Geographic information retrieval (GIR) methods have focused on this issue; however, they sometimes fail to solve the problem because the spatial and textual similarities of words are considered and calculated separately. In this paper, from the perspective of spatial context, we consider the two parts as a whole-spatial context semantics, and we propose a method that measures spatial semantic similarity using a sliding geospatial context window for geo-tagged words. The proposed method was first validated with a set of simulated data and then applied to a real-world dataset from Flickr. As a result, a spatial semantic similarity model at different scales is presented. We believe this model is a necessary supplement for traditional textual-language semantic analyses of words obtained by word-embedding technologies. This study has the potential to improve the quality of recommendation systems by considering relevant spatial context semantics, and benefits linguistic semantic research by emphasising the spatial cognition among words.</description><subject>Algorithms</subject><subject>Bars</subject><subject>Biology and Life Sciences</subject><subject>Cognition</subject><subject>Cognition & reasoning</subject><subject>Comprehension</subject><subject>Computer and Information Sciences</subject><subject>Computer simulation</subject><subject>Context</subject><subject>Earth Sciences</subject><subject>Electric power</subject><subject>Embedding</subject><subject>Environmental science</subject><subject>Humans</subject><subject>Information processing</subject><subject>Information retrieval</subject><subject>Information Storage and Retrieval</subject><subject>Keywords</subject><subject>Language</subject><subject>Linguistics - trends</subject><subject>Medicine and Health Sciences</subject><subject>Natural 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The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial semantics of implied spatial information are rarely preserved. Geographic information retrieval (GIR) methods have focused on this issue; however, they sometimes fail to solve the problem because the spatial and textual similarities of words are considered and calculated separately. In this paper, from the perspective of spatial context, we consider the two parts as a whole-spatial context semantics, and we propose a method that measures spatial semantic similarity using a sliding geospatial context window for geo-tagged words. The proposed method was first validated with a set of simulated data and then applied to a real-world dataset from Flickr. As a result, a spatial semantic similarity model at different scales is presented. 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| subjects | Algorithms Bars Biology and Life Sciences Cognition Cognition & reasoning Comprehension Computer and Information Sciences Computer simulation Context Earth Sciences Electric power Embedding Environmental science Humans Information processing Information retrieval Information Storage and Retrieval Keywords Language Linguistics - trends Medicine and Health Sciences Natural language Natural Language Processing PubMed Recommender systems Research and Analysis Methods Semantics Similarity Social Sciences Software Spatial data Visualization Words (language) |
| title | Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context window |
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