Interpreting TF-IDF term weights as making relevance decisions

By Ho Chung Wu, Robert Wing Pong Luk, Kam-Fai Wong, Kui-Lam Kwok. TOIS, 2008

872 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f6bbbf2cc785cf96019dcd9c41ab1801aad962dd

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Citations in the corpus

Where to find my next passenger (2011, background): “inverse document frequency (tf-idf)[10] to measure the importance of a POI to a parking place.”
CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain (2019, background): “Intuitively traditional approaches calculate word-to-word similarity with vector space model, e.g. term frequency-inverse document frequency (Wu et al., 2008), bag-of-words (Bilotti et al., 2007).”
T-Finder: A Recommender System for Finding Passengers and Vacant Taxis (2013, background): “This section details the process for detecting parked status from a nonoccupied trip and accordingly finding out the parking places in the urban area of a city based on a collection of taxi trajectories.”
N-gram IDF: A Global Term Weighting Scheme Based on Information Distance (2015, methodology): “In addition to the effectiveness of TF-IDF in various applications such as information retrieval [46], document clustering [14], key term extraction [19] and object matching in videos [44], many theoretical explanations [2, 20, 36, 38] encourage researchers and developers to employ TF-IDF.”
Semantic Text Matching for Long-Form Documents (2019, background): “[49] exploit the vector space model with the term frequency-inverse document frequency (TF-IDF).”
Text Classification Algorithms: A Survey (2019, background): “Weighted words computes document similarity directly from the word-count space which increases the computational time for large vocabularies [222].”
Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security (2017, methodology): “Since the learning problem of detecting the security level of code snippets can be viewed as a document classification problem, we employ a common feature extraction method named tf-idf vectorizer [33].”
Text summarization using unsupervised deep learning (2017, background): “It represents each word as an element of a vector comprising of its term frequency in the document, as well as over all documents (idf ) (Wu et al., 2008).”
Malgta: large language model-based guided malware tactical analysis (2025, unknown): “This information retrieval technology efficiently and accurately locates relevant information [24, 25].”