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يعرض 1 - 8 نتائج من 8 نتيجة بحث عن '(((("python code implementing") OR ("python model implementing"))) OR ("python table represents"))', وقت الاستعلام: 0.29s تنقيح النتائج
  1. 1
    Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’), followed by a word frequency count (‘Counter’).

    Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’... حسب Enock Niyonkuru (21246879)

    منشور في 2025
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  2. 2
    Schematic of the approach: This schematic illustrates the entire workflow of the project.

    Schematic of the approach: This schematic illustrates the entire workflow of the project. حسب Enock Niyonkuru (21246879)

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  3. 3
    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739).

    Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). حسب Enock Niyonkuru (21246879)

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  4. 4
    Comparative analysis of WordNet replacement impact on data distribution.

    Comparative analysis of WordNet replacement impact on data distribution. حسب Enock Niyonkuru (21246879)

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  5. 5
    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10, this Table shows the results for three different window sizes at a =. Abbreviations are the same as for Table 1.

    Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... حسب Enock Niyonkuru (21246879)

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  6. 6
    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet.

    Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet. حسب Enock Niyonkuru (21246879)

    منشور في 2025
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  7. 7
    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,918. The Table summarizes results for different thresholds () and categories of concept/gene sets (M,B,K,G,P). Columns: : Replacement threshold; replaced: Unique Replaced Concepts; Category: M = MeSH, B = Biocarta, K = KEGG, G = GP(bp), P = PID; # sets: Number of concept/gene sets in the categories; #Concepts: number of concept vectors in the category; <i>WN better</i><i>: The count and percentage of concept/gene sets for which the mean interconcept distance was smaller for WN than for PM. “Winners” are shown in bold.;</i> <i>PM better</i><i>: Analogous to “WN better” but for PM.</i>

    Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... حسب Enock Niyonkuru (21246879)

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  8. 8
    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of concept sets where the cluster mean distance is significantly lower, indicative of superior embeddings.

    Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... حسب Enock Niyonkuru (21246879)

    منشور في 2025
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