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    Platform For AI:Keyword extraction

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    Platform For AI:Keyword extraction

    Last Updated:Mar 05, 2026

    Keyword extraction is a natural language processing (NLP) technique. It identifies and extracts words from a text that are highly relevant to the main topic. This technique often uses the TextRank algorithm. TextRank builds a word co-occurrence network and applies a calculation method similar to PageRank to evaluate the importance of each word. Words with higher weights are selected as keywords. This method helps you understand and summarize large amounts of text.

    The common workflow is as follows:

    1. Source data

    2. Tokenize the text.

    3. Filter the words.

    4. Extract keywords.

    Component configuration

    Method 1: Using the GUI

    On the Designer workflow page, you can add the Keyword Extraction component and configure its parameters in the pane on the right.

    Parameter type

    Parameter

    Description

    Fields setting

    Document ID column

    The name of the column that contains document IDs.

    The word segmentation results for the article content.

    The name of the column that contains the tokenized document content.

    Parameters setting

    Number of keywords to output

    An integer. Default value: 5.

    Window size

    An integer. Default value: 2.

    Damping coefficient

    Default value: 0.85.

    Maximum iterations

    Default value: 100.

    Convergence coefficient

    Default value: 0.000001.

    Execution tuning

    Number of cores. Auto-assigned by default.

    Selected by default.

    Memory per core. Auto-assigned by default.

    Selected by default.

    Method 2: Using PAI commands

    You can use PAI commands to configure the parameters for the Keyword Extraction component. You can use the SQL script component to call PAI commands. For more information, see SQL Script.

    PAI -name KeywordsExtraction      
    -DinputTableName=maple_test_keywords_basic_input    
    -DdocIdCol=docid -DdocContent=word    
    -DoutputTableName=maple_test_keywords_basic_output    
    -DtopN=19;

    Parameter

    Required

    Default value

    Description

    inputTableName

    Yes

    None

    The input table.

    inputTablePartitions

    No

    All partitions

    The partitions in the input table to use for training. Use the Partition_name=value format. For multi-level partitions, use name1=value1/name2=value2. Separate multiple partitions with commas (,).

    outputTableName

    Yes

    None

    The name of the output table.

    docIdCol

    Yes

    None

    The name of the column that contains document IDs. You can specify only one column.

    docContent

    Yes

    None

    The Word column. You can specify only one column.

    topN

    No

    5

    The number of top keywords to return. If the total number of keywords is less than this value, all keywords are returned.

    windowSize

    No

    2

    The window size for the TextRank algorithm.

    dumpingFactor

    No

    0.85

    The damping coefficient for the TextRank algorithm.

    maxIter

    No

    100

    The maximum number of iterations for the TextRank algorithm.

    epsilon

    No

    0.000001

    The convergence residual threshold for the TextRank algorithm.

    lifecycle

    No

    None

    The lifecycle of the output table.

    coreNum

    No

    Calculated automatically

    Worker count.

    memSizePerCore

    No

    Calculated automatically

    The memory size per worker, in MB.

    Example

    1. Generate data

      In the input table, separate words with spaces. Filter out stop words, such as 'the' and 'a', and all punctuation marks.

      docid:string

      word:string

      doc0

      blended-wing-body aircraft is future aviation field development a new direction many research institutions have started on blended-wing-body aircraft research and its fully-automatic shape optimization algorithm has become a new research hot-spot existing achievements basis on top of analyze compare common modeling solving platform usage methods and features design write blended-wing-body aircraft shape optimization geometric modeling grid division flow-field solving shape optimization module compare different algorithms between pros and cons implement blended-wing-body aircraft conceptual-design in shape optimization geometric modeling and grid generation module implement based-on transfinite interpolation grid generation algorithm based-on spline curve modeling method flow-field solving module includes finite difference solver finite element solver and panel method solver among them finite difference solver mainly includes based-on finite difference method potential-flow mathematical modeling based-on Cartesian grid variable step-size difference format derivation Cartesian grid generation index algorithm based-on Cartesian grid Neumann boundary-condition expression form derivation implement based-on finite difference solver two-dimensional airfoil aerodynamic parameters calculation example finite element solver mainly includes based-on variational principle potential-flow finite element theory modeling two-dimensional finite element Kutta condition expression derivation based-on least squares velocity solving algorithm design based-on Gmsh two-dimensional with-wake airfoil spatial grid generator development implement based-on finite element solver two-dimensional airfoil aerodynamic parameters calculation example panel method solver mainly includes based-on panel method potential-flow theory modeling automatic wake generation algorithm design based-on panel method three-dimensional blended-wing-body body flow-field solver development based-on Blasius flat-plate solution drag estimation algorithm design solver Fortran language on port Python and Fortran code mixed-compilation based-on OpenMP and CUDA parallel acceleration algorithm design and development implement based-on panel method solver three-dimensional blended-wing-body body aerodynamic parameters calculation example shape optimization module implemented based-on free form deformation grid deformation algorithm genetic-algorithm differential evolution algorithm aircraft surface-area calculation algorithm based-on moment integration aircraft volume calculation algorithm development based-on VTK data visualization format tool

    2. PAI command

      PAI -name KeywordsExtraction      
    -DinputTableName=maple_test_keywords_basic_input    
    -DdocIdCol=docid -DdocContent=word    
    -DoutputTableName=maple_test_keywords_basic_output    
    -DtopN=19;

    3. Output description

      docid

      keywords

      weight

      doc0

      based-on

      0.041306752223538405

      doc0

      algorithm

      0.03089845626854151

      doc0

      modeling

      0.021782865850562882

      doc0

      grid

      0.020669749212693957

      doc0

      solver

      0.020245609506360847

      doc0

      aircraft

      0.019850761705313365

      doc0

      research

      0.014193732541852615

      doc0

      finite element

      0.013831122054200538

      doc0

      solving

      0.012924593244133104

      doc0

      module

      0.01280216562287212

      doc0

      derivation

      0.011907588923852495

      doc0

      shape

      0.011505456605632607

      doc0

      difference

      0.011477831662367547

      doc0

      potential-flow

      0.010969269350293957

      doc0

      design

      0.010830986516637251

      doc0

      implement

      0.010747536556701583

      doc0

      two-dimensional

      0.010695570768457084

      doc0

      development

      0.010527342662670088

      doc0

      new

      0.010096978306668461