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1
Metadata record for: The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies ...
Scientific Data Curation Team. - : figshare, 2020
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Metadata record for: The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies ...
Scientific Data Curation Team. - : figshare, 2020
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3
R Markdown Notebook and data for "Contrasting the semantics of Indonesian - kan and - i verb pairs: A usage-based, constructional approach" ...
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4
R Markdown Notebook and data for "Contrasting the semantics of Indonesian - kan and - i verb pairs: A usage-based, constructional approach" ...
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5
Metadata record for: The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies ...
Scientific Data Curation Team. - : figshare, 2019
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6
Dataset for Vector space model and the usage patterns of Indonesian denominal verbs ...
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7
Dataset for Vector space model and the usage patterns of Indonesian denominal verbs ...
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8
R codes and figures for Distinctive Metaphors for HAPPINESS near-synonyms in Indonesian: A quantitative corpus-based approach ...
Rajeg, Gede Primahadi Wijaya. - : Monash University, 2019
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9
Semantic Similarity Measurement Using Historical Google Search Patterns ...
Martinez, Jorge. - : figshare, 2018
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10
Semantic Similarity Measurement Using Historical Google Search Patterns ...
Martinez, Jorge. - : figshare, 2018
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11
An Overview of Textual Semantic Similarity Measures Based on Web Intelligence ...
J. M. Gil. - : figshare, 2018
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12
Semantic Similarity Measurement Using Historical Google Search Patterns ...
Gil, Jorge Martinez. - : figshare, 2018
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13
Semantic Similarity Measurement Using Historical Google Search Patterns ...
Gil, Jorge Martinez. - : figshare, 2018
Abstract: Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is an important challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do this, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment ...
Keyword: 80105 Expert Systems; 80604 Database Management; 80605 Decision Support and Group Support Systems; 80606 Global Information Systems; 80704 Information Retrieval and Web Search; FOS Computer and information sciences; FOS Media and communications; Information Systems
URL: https://figshare.com/articles/Semantic_Similarity_Measurement_Using_Historical_Google_Search_Patterns/6652373
https://dx.doi.org/10.6084/m9.figshare.6652373
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14
CoTO: A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures ...
Hagenberg, Jorge. - : figshare, 2018
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15
An Overview of Textual Semantic Similarity Measures Based on Web Intelligence ...
J. M. Gil. - : figshare, 2018
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16
CoTO: A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures ...
Hagenberg, Jorge. - : figshare, 2018
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