What is the use of TF IDF
TF-IDF stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic that is used in Natural Language Processing (NLP) and information retrieval to evaluate the importance of a word in a document relative to a collection of documents (corpus).
Here's how TF-IDF is calculated and its use:
-
Term Frequency (TF):
- Term Frequency measures how frequently a term (word) appears in a document. It is calculated by dividing the number of times a term occurs in a document by the total number of terms in the document. TF helps to understand the importance of a word within a document.
TF(t,d)=Total number of terms in document d
Top Questions From What is the use of TF IDF
- Give examples of any two real world applications of NLP
- What is tokenization in NLP
- What is the difference between a formal language and a natural language
- What is the difference between stemming and lemmatization
- What is NLU
- List the differences between NLP and NLU
- What do you know about Latent Semantic Indexing
- List a few methods for extracting features from a corpus for NLP
- What are stop words
- What do you know about Dependency Parsing
- What is Text Summarization
- What are false positives and false negatives
- List a few methods for part-of speech tagging
- What is a corpus
- List a few real-world applications of the n gram model
- What does TFIDF stand for
- What is perplexity in NLP
- Which algorithm in NLP supports bidirectional context
- What is the Naive Bayes algorithm
- What is Part of Speech tagging
- What is the bigram model in NLP
- What is the significance of the Naive Bayes algorithm in NLP
- What do you know about the Masked Language Model
- What is the Bag of words model in NLP
- Briefly describe the N gram model in NLP
- What is the Markov assumption for the bigram model
- What do you understand by word embedding
- What is an embedding matrix
- List a few popular methods used for word embedding
- How will you use Python’s concordance command in NLTK for a text that does not belong to the package
- Write the code to count the number of distinct tokens in a text
- What are the first few steps that you will take before applying an NLP machine-learning algorithm to a given corpus
- For correcting spelling errors in a corpus
- which one is a better choice: a giant dictionary or a smaller dictionary
- Do you always recommend removing punctuation marks from the corpus you’re dealing with
- List a few libraries that you use for NLP in Python
- Suggest a few machine learning/deep learning models that are used in NLP
- Which library contains the Word2Vec model in Python
- What are homographs homophones and homonyms
- Is converting all text in uppercase to lowercase always a good idea
- What is a hapax hapax legomenon
- Is tokenizing a sentence based on white-space
- What is a collocation
- List a few types of linguistic ambiguities
- What is a Turing Test
- What do you understand by regular expressions in NLP
- Differentiate between orthographic rules and morphological rules with respect to singular and plural forms of English words
- Define the term parsing concerning NLP
- Use the minimum distance algorithm to show how many editing steps it will take for the word ‘intention’ to transform into ‘execution
- Calculate the Levenshtein distance between two sequences ‘intention’ and ‘execution’
- What are the full listing hypothesis and minimum redundancy hypothesis
- What are some most common areas of usage of Natural Language Processing
- What are some of the major components of Natural Language Processing
- What do you understand by NLTK in Natural Language Processing
- What are the most used Natural Language Processing Terminologies
- What is the difference between formal and natural languages
- What is the use of TF IDF
- What is the full form of NLP
- What are the tools used for training NLP models
- What is Bag of Words in Natural Language Processing
- What do you understand by Dependency Parsing in Natural Language Processing
- What do you understand by semantic analysis
- What are the stop words in Natural Language Processing
- What do you understand by information extraction
- What is NES in Natural Language Processing
- What is pragmatic ambiguity in NLP
- What are the techniques used for semantic analysis
- What are the various models of information extraction
- What are the most commonly used models to reduce data dimensionality in NLP
- What is language modeling in NLP
- What is Lemmatization in Natural Language Processing
- What do you understand by MLM in Natural Language Processing
- What is the difference between Stemming and Lemmatization in NLP
- What is Stemming in Natural Language Processing
- What is Latent Semantic Indexing
- What is tokenization in Natural Language Processing
- What is the key difference between dependency parsing and shallow parsing
- What are the best open sources of NLP Tools available in the market
- What are some opensource libraries used in NLP
- What are the three main purposes of an operating system
- What are the main differences between operating systems for mainframe computers and personal computers
- List the four steps that are necessary to run a program on a completely dedicated machine
- What is the main difficulty that a programmer must overcome in writing an operating system for a real-time environment
- How does the distinction between kernel mode and user mode function as a rudimentary form of protection
- Which of the following instructions should be privileged
- Is the Internet a LAN or a WAN
- What is the purpose of system calls
- What are the five major activities of an operating system in regard to process management
- What are the three major activities of an operating system in regard to memory management
- What are the three major activities of an operating system in regard to secondary-storage management
- What is the purpose of system programs
- What is the main advantage of the layered approach to system design? What are the disadvantages of using the layered approach
- What are the main differences between capability lists and access lists
- What protection problems may arise if a shared stack is used for parameter passing
- Capability lists are usually kept within the address space of the user. How does the system ensure that the user cannot modify the contents of the list
- What type of operating system is Windows XP
- List the design goals of Windows XP
- What are the responsibilities of the IO manager
- How does NTFS handle data structures
- Lmmm
- hi
- tes
- y
- Priority Queues and Hashtables
- Priority Queues and Hashtables
- Priority Queues and Hashtables
- mini Replicated Reliable Banking System
- Digital Electronics
- Data Modeling
Tags
Qualification
Course
Department
Stream
Subject