割让
发表于 2025-3-25 03:59:34
https://doi.org/10.1007/978-3-030-46527-8need to identify places matching some criteria, to identify them in space or relative to other places, or to characterize the qualities of such places. Human place-related questions have thus far been insufficiently studied and differ strongly from typical keyword queries. They thus challenge today’
物种起源
发表于 2025-3-25 10:00:51
Negative Electrodes in Lithium Systems,ion answering remained nearly untouched although geographic questions account for a substantial part of daily communication. Compared to general QA systems, geographic QA has its own uniqueness, one of which can be seen during the process of handling unanswerable questions. Since users typically foc
HEW
发表于 2025-3-25 11:38:53
Electromagnetic Energy Storage, words. Word embeddings have been mostly used to solve traditional NLP problems, such as question answering, textual entailment and sentiment analysis. This work proposes a new way of thinking about word embeddings that exploits them in order to represent geographical knowledge (e.g., geographical o
IVORY
发表于 2025-3-25 19:26:39
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Offensive
发表于 2025-3-25 22:58:35
W. H. Bostick,V. Nardi,O. S. F. Zuckeremergence of passively recorded movement data with spatio-temporal and semantic information offers opportunities for uncovering individuals’ travel mode choice behavior. Considering that many of these choices are highly regular and are performed in similar manners by different groups of people, it i
Mumble
发表于 2025-3-26 02:49:07
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针叶树
发表于 2025-3-26 05:12:25
Conference proceedings 2020ience, held in June 2019 at Cyprus University of Technology, Limassol, Cyprus. It is intended primarily for professionals and researchers in geographic information science, as well as those in related fields in which geoinformation application plays a significant role. .
BURSA
发表于 2025-3-26 11:02:31
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PUT
发表于 2025-3-26 14:24:36
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Hiatal-Hernia
发表于 2025-3-26 19:44:52
Evaluating the Effectiveness of Embeddings in Representing the Structure of Geospatial Ontologies words. Word embeddings have been mostly used to solve traditional NLP problems, such as question answering, textual entailment and sentiment analysis. This work proposes a new way of thinking about word embeddings that exploits them in order to represent geographical knowledge (e.g., geographical o