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  • Coherence_Evaluations - nws_random5k.csv

    Dataset thumbnail: Coherence_Evaluations - nws_random5k.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one from) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] / News [nws] SearchTerm[s] = (one from) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/04/2022
    更改日期:
    11/04/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Coherence_Evaluations - nws_pollution.csv

    Dataset thumbnail: Coherence_Evaluations - nws_pollution.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one from) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] / News [nws] SearchTerm[s] = (one from) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/03/2022
    更改日期:
    11/17/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Coherence_Evaluations - nws_earth.csv

    Dataset thumbnail: Coherence_Evaluations - nws_earth.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one from) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] / News [nws] SearchTerm[s] = (one from) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/03/2022
    更改日期:
    11/03/2022
    创建:
    2022
    证书:
    Open Data Commons Attribution License (ODC-By)

  • Coherence_Evaluations - nws_climate.csv

    Dataset thumbnail: Coherence_Evaluations - nws_climate.csv
    Type:
    Dataset
    摘抄:
    CSV files containing the topic coherence scoring pertaining to datasets of: DocumentCount = 5,000 Corpus = (one of) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] SearchTerm[s] = (one of) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus Coherence was scored across every combination of: TopicCount: 10-40 Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric] The columns in this file include: Validation_Set: Which search term this scoring pertains to Topics: Number of topics in the model Alpha: Hyperparameter alpha selection from the 6 options above Beta: Hyperparameter beta selection from the 6 options above Coherence: The topic coherence score for the given model-row Perplexity: The perplexity score for the given model-row
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    11/02/2022
    更改日期:
    11/02/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • Figure Z. Topic Coherence across Hyperparameter:Beta

    Image thumbnail: Figure Z. Topic Coherence across Hyperparameter:Beta
    Type:
    Image
    摘抄:
    Box-and-Whisker visualization of coherence scores for three corpora types: Caselaw (cas), Pubmed Abstracts (pma), Pubmed Central (pmc). This figure is for models matching search-term "climate". Visualizations for other search terms and additional interactive elements available at the related URL below. Coherence was scored across every combination of: - TopicCount: 10-40 - Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] - Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric]
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/12/2022
    更改日期:
    08/12/2022
    创建:
    2022
    证书:
    Public Domain Mark 1.0

  • Figure Y. Topic Coherence across Hyperparameter:Alpha

    Image thumbnail: Figure Y. Topic Coherence across Hyperparameter:Alpha
    Type:
    Image
    摘抄:
    Box-and-Whisker visualization of topic coherence scores for three corpora types: Caselaw (cas), Pubmed Abstracts (pma), Pubmed Central (pmc). This figure is for models matching search-term "climate". Visualizations for other search terms and additional interactive elements available at related URL below. Coherence was scored across every combination of: - TopicCount: 10-40 - Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] - Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric]
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/12/2022
    更改日期:
    08/12/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • Figure X. Median Coherence

    Image thumbnail: Figure X. Median Coherence
    Type:
    Image
    摘抄:
    Heat map visualization of median coherence scores for three corpora: Caselaw (cas), Pubmed Abstracts (pma), Pubmed Central (pmc). Median coherence scores across all search-term based models ("climate", "earth", "environmental" "pollution") The median is found from 1,116 total coherence scores. Coherence was scored across every combination of: - TopicCount: 10-40 - Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric] - Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric]
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Public Domain Mark 1.0

  • central_pmc_pollution20.csv

    Dataset thumbnail: central_pmc_pollution20.csv
    Type:
    Dataset
    摘抄:
    The data files in this collection are for datasets: Document Count: 5,000 documents Corpus: (one of) Caselaw (cas) / Pubmed Abstracts (pma) / Pubmed Central (pmc) Search Term: (one of) Climate / Earth / Environmental / Pollution Networked Models at Topic Counts: 15, 20 The filename convention is: central_datasetAcronym_searchTerm,topicCount.csv Each row = one topic-node of the networked model The columns in each CSV include: Edges: Connection threshold [e.g. 25% means that row depicts nodes' centrality when the edges in the graph are in the top quartile of strength] ID: Unique ID for the topic-node in the networked model Score: The betweenness centrality score of that topic-node Terms: The top terms of the topic-node
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • central_pmc_environmental15.csv

    Dataset thumbnail: central_pmc_environmental15.csv
    Type:
    Dataset
    摘抄:
    The data files in this collection are for datasets: Document Count: 5,000 documents Corpus: (one of) Caselaw (cas) / Pubmed Abstracts (pma) / Pubmed Central (pmc) Search Term: (one of) Climate / Earth / Environmental / Pollution Networked Models at Topic Counts: 15, 20 The filename convention is: central_datasetAcronym_searchTerm,topicCount.csv Each row = one topic-node of the networked model The columns in each CSV include: Edges: Connection threshold [e.g. 25% means that row depicts nodes' centrality when the edges in the graph are in the top quartile of strength] ID: Unique ID for the topic-node in the networked model Score: The betweenness centrality score of that topic-node Terms: The top terms of the topic-node
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)

  • central_pmc_environmental20.csv

    Dataset thumbnail: central_pmc_environmental20.csv
    Type:
    Dataset
    摘抄:
    The data files in this collection are for datasets: Document Count: 5,000 documents Corpus: (one of) Caselaw (cas) / Pubmed Abstracts (pma) / Pubmed Central (pmc) Search Term: (one of) Climate / Earth / Environmental / Pollution Networked Models at Topic Counts: 15, 20 The filename convention is: central_datasetAcronym_searchTerm,topicCount.csv Each row = one topic-node of the networked model The columns in each CSV include: Edges: Connection threshold [e.g. 25% means that row depicts nodes' centrality when the edges in the graph are in the top quartile of strength] ID: Unique ID for the topic-node in the networked model Score: The betweenness centrality score of that topic-node Terms: The top terms of the topic-node
    作者:
    McCabe, Erin E.
    提交者:
    Erin E. McCabe
    上传日期:
    08/10/2022
    更改日期:
    08/10/2022
    创建:
    2022
    证书:
    Open Data Commons Public Domain Dedication and License (PDDL)