TF-IDF-based Text-Item Feature Generation Algorithm

A TF-IDF-based Text-Item Feature Generation Algorithm is a text-item feature generation algorithm that applies term frequency-inverse document frequency.

• Context:
  • It can (often) be employed for tasks like information retrieval, text classification, and content-based recommendation systems.
  • It can assign higher weights to frequent terms in a document but less so across the corpus.
  • ...
• Example(s):
  • one that includes TF-IDF Vector Normalization: Utilizing L2 normalization to scale TF-IDF vectors into a comparable scale.
  • one that includes a TF-IDF Smoothing Technique: Implementing smoothing in IDF calculation to prevent division by zero and moderate the weight of common terms.
  • one that includes Sublinear Scaling: Employing sublinear TF scaling to reduce the bias in longer documents.
  • one that includes N-grams Integration: Using n-grams as features in TF-IDF algorithms to capture more contextual information in text classification.
  • one that includes Feature Selection Algorithm: Applying feature selection methods like chi-squared tests to enhance the relevance of features in the TF-IDF algorithm.
  • one that includes Dimensionality Reduction via NNMF (Non-Negative Matrix Factorization).
  • ...
• Counter-Example(s):
  • A Word Embedding-based Text Feature Generation Algorithm.
  • A Topic Modeling Algorithm.
• See: Text Classification, Information Retrieval, Feature Extraction in NLP, Document Frequency, Term Frequency.

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References

2023

• (Foysal & Böck, 2023) ⇒ Abdullah Al Foysal, and Ronald Böck. (2023). “Who Needs External References? Text Summarization Evaluation Using Original Documents.” In: AI, 4(4). doi:10.3390/ai4040049
  • NOTE: It evaluates the performance of automatic evaluation metrics for text summarization, emphasizing the role of TF-IDF.

2016

• https://www.gabormelli.com/RKB/2016_DEFEXTASemiSupervisedDefinition
  • NOTE: It discusses a Conditional Random Fields based sequential labeling algorithm where TF-IDF plays a role in the bootstrapping process.

2014

• http://www.gabormelli.com/RKB/2014_ALatentSemanticModelwithConvolu
  • NOTE: It explains the convolution operation as a feature extraction method, including using TF-IDF for contextual analysis.

2003

• https://www.gabormelli.com/RKB/2003_AComparisonOfStringEditDistMetrics
  • NOTE: It covers a study on a hybrid scheme combining TFIDF weighting and string edit distance metrics for information retrieval efficiency.

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