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04_​Word_​Embedding_​Clax

04_Word_Embedding_Classification

Word Embedding for Classification

Word2Vec Learner node here is trained on a training set extracted from the Human AIDS vs. Mouse Cancer dataset to provide word embedding.
Each Document is then represented by only three keyords, which are then embedded using the Word Vector Apply.
After splitting the value collections, word emebedding is used to train a decision tree to classify articles on human AIDS vs. articles on mouse cancer.

Word Embedding for Classification Word2Vec Learner node here is trained on a training set extracted from the Human AIDS vs. Mouse Cancer dataset to provide word embedding. Each Document is then represented by only three keyords, which are then embedded using the Word Vector Apply.After splitting the value collections, word emebedding is used to train a decision tree to classify articles on human AIDS vs. articles on mouse cancer. Training Set Test Set Classifying article Category (Human AIDS vs. Mouse Cancer) based on word embedding provided by Word2Vec Learner Read articles from PubmedPubmed_Articles.csvcleaningstemmingtag filteringColor by Categorylabelclass = CategoryTraining 80/ test set 20tag keywordsjust keepkeywordsin Documentstag keywordsjust keepkeywordsin Documentsonly 3 keywordsper DocumentUsing Word Vector ApplyUsing Word Vector Applytraining on fulltraining setjoin backCategoryon RowIDjoin backCategoryon RowID Reading Data Pre-processing Color Appender Color Manager Decision TreePredictor DecisionTree Learner Partitioning Dictionary Tagger Tag Filter Dictionary Tagger Tag Filter Keyword Vocabulary Word Embedding Word Embedding Zero-padding Word2Vec Learner Scorer Joiner Joiner Word Embedding for Classification Word2Vec Learner node here is trained on a training set extracted from the Human AIDS vs. Mouse Cancer dataset to provide word embedding. Each Document is then represented by only three keyords, which are then embedded using the Word Vector Apply.After splitting the value collections, word emebedding is used to train a decision tree to classify articles on human AIDS vs. articles on mouse cancer. Training Set Test Set Classifying article Category (Human AIDS vs. Mouse Cancer) based on word embedding provided by Word2Vec Learner Read articles from PubmedPubmed_Articles.csvcleaningstemmingtag filteringColor by Categorylabelclass = CategoryTraining 80/ test set 20tag keywordsjust keepkeywordsin Documentstag keywordsjust keepkeywordsin Documentsonly 3 keywordsper DocumentUsing Word Vector ApplyUsing Word Vector Applytraining on fulltraining setjoin backCategoryon RowIDjoin backCategoryon RowID Reading Data Pre-processing Color Appender Color Manager Decision TreePredictor DecisionTree Learner Partitioning Dictionary Tagger Tag Filter Dictionary Tagger Tag Filter Keyword Vocabulary Word Embedding Word Embedding Zero-padding Word2Vec Learner Scorer Joiner Joiner

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Created by: Kilian
Created at: 2014-10-20
On NodePit since: 2024-06-28
Last update: 2025-07-15
Created with KNIME version: v4.5.0
Tags: BooksFrom Words To WisdomText Mining

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