Understanding TF-IDF Computation in Latent Semantic Indexing

Feb 01, 2026

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This project delves into the computational method of Term Frequency-Inverse Document Frequency (TF-IDF) for latent semantic indexing. The focus is on calculating TF-IDF for a token in a given document by utilizing the formula: TF(token_i, document_j) = tf(ti, dj) * log(|Tr| / |Tr(ti)|). Furthermore, it discusses crucial aspects such as sorting tokens, determining their frequencies within documents, and setting thresholds for inclusion in calculations. The aim is to better understand the significance of numerical values in the context of information retrieval and document representation.

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Understanding TF-IDF Computation in Latent Semantic Indexing

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Project Description 3Latent Semantic Index
Compute TFIDF(token_i, document_j) = TF(ti; dj)log |Tr|/|Tr(ti)| The token in each file is sorted and attached the TFIDF value
TFIDF 1. Tr(ti)= the # of documents in Tr in which ti occurs at least once, =1 +log(N(ti; dj))if N(ti; dj)> 0 2. TF(ti; dj)  =0 otherwise 3. N(ti, dj) = the frequency(# OF OCCURRENCES OF ti / # OF TOKENs indj of ti in dj.
Important point about Token • TFIDF(token_i, document_j) = tf(ti; dj)log |Tr|/|Tr(ti)| Correction(only consider (threshold2??) >=|Tr(ti)| >= threshold1 Discuss come properties about this numerical values (Tr set of the documents; Tr(ti) the set of documents containing ti)
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