Learn how Term Frequency-Inverse Document Frequency (TF-IDF) calculates the importance of terms in a document for NLP tasks. Discover its role and applications.
Table of Contents
Question
Which NLP technique involves calculating a score to know the significance of a term in a specific file?
A. Latent Semantic Analysis (LSA)
B. Part-of-Speech (POS) tagging
C. Term Frequency-Inverse Document Frequency (TF-IDF)
D. Syntax Tree Parsing
Answer
C. Term Frequency-Inverse Document Frequency (TF-IDF)
Explanation
Term Frequency-Inverse Document Frequency (TF-IDF) is a score that shows how important a word is to a specific document compared to its importance in a collection of documents. It is calculated by multiplying the term frequency (how often a word appears in a document) by the inverse document frequency (a measure of how rare the word is in the entire collection of documents). TF-IDF helps identify the significance of a term within a specific document.
Part-of-Speech (POS) tagging is an NLP technique that assigns a grammatical category (such as a noun, verb, or adjective) to each word in a sentence. It focuses on understanding the syntactical structure of the text by classifying words based on their grammatical roles. POS tagging focuses on the grammatical categorization of words, not on calculating a score based on term frequency across documents.
Latent Semantic Analysis (LSA) is a technique that analyzes relationships between terms and concepts within a collection of documents. It identifies hidden patterns and relationships by applying singular value decomposition to a term-document matrix. LSA helps computers understand the hidden meaning behind words in text, like the difference between “bank” the financial institution and “bank” of a river. It goes beyond just looking at individual words and instead analyzes how words are used together to convey ideas. This allows LSA to understand synonyms and related concepts, even if the exact words aren’t used. It does not calculate a score based on term frequency.
Syntax Tree Parsing involves analyzing the syntactical structure of a sentence by breaking it down into a hierarchical tree-like form. It helps in understanding the grammatical relationships and dependencies between words in a sentence. However, it does not directly involve calculating a score based on term frequency across documents.
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