Extracting Place Functionality From Crowdsourced Textual Data Using Semantic Space Modeling

Karimi, Mina; Mesgari, Mohammad Saadi

doi:10.1109/access.2023.3332854

Cited by 1 publication

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“…Of this, a noticeable share is enriched with spatial information. This information is mainly latent in textual content in the form of online books, user reviews, and blog posts [11]. As users utilize a natural language to communicate through the internet, this information appears in terms of qualitative descriptions of objects and events [12].…”

Section: Introductionmentioning

confidence: 99%

Get Spatial from Non-Spatial Information: Inferring Spatial Information from Textual Descriptions by Conceptual Spaces

Abbasi,

Alesheikh,

Razavi-Termeh

2023

Mathematics

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With the rapid growth of social media, textual content is increasingly growing. Unstructured texts are a rich source of latent spatial information. Extracting such information is useful in query processing, geographical information retrieval (GIR), and recommender systems. In this paper, we propose a novel approach to infer spatial information from salient features of non-spatial nature in text corpora. We propose two methods, namely DCS and RCS, to represent place-based concepts. In addition, two measures, namely the Shannon entropy and the Moran’s I, are proposed to calculate the degree of geo-indicativeness of terms in texts. The methodology is compared with a Latent Dirichlet Allocation (LDA) approach to estimate the accuracy improvement. We evaluated the methods on a dataset of rental property advertisements in Iran and a dataset of Persian Wikipedia articles. The results show that our proposed approach enhances the relative accuracy of predictions by about 10% in case of the renting advertisements and by 13% in case of the Wikipedia articles. The average distance error is about 13.3 km for the advertisements and 10.3 km for the Wikipedia articles, making the method suitable to infer the general region of the city in which a property is located. The proposed methodology is promising for inferring spatial knowledge from textual content that lacks spatial terms.

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Section: Introductionmentioning

confidence: 99%

Get Spatial from Non-Spatial Information: Inferring Spatial Information from Textual Descriptions by Conceptual Spaces

Abbasi,

Alesheikh,

Razavi-Termeh

2023

Mathematics

View full text Add to dashboard Cite

show abstract

Extracting Place Functionality From Crowdsourced Textual Data Using Semantic Space Modeling

Abstract: This paragraph of the first footnote will contain support information, including sponsor and financial support acknowledgment.

Cited by 1 publication

References 58 publications

Get Spatial from Non-Spatial Information: Inferring Spatial Information from Textual Descriptions by Conceptual Spaces

Get Spatial from Non-Spatial Information: Inferring Spatial Information from Textual Descriptions by Conceptual Spaces

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