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Natural Language Processing (NLP), Top Exam Questions and answers, verified.

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Natural Language Processing (NLP), Top Exam Questions and answers, verified. Artificial Intelligence - -A computer performing tasks that a human can do NLP Sentiment analysis is a form of... - -classification NLP topic modeling is a form of... - -Dimensionality reduction Tokenization ...

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  • June 13, 2023
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Natural Language Processing (NLP), Top
Exam Questions and answers, verified.

Artificial Intelligence - ✔✔-A computer performing tasks that a human can do



NLP Sentiment analysis is a form of... - ✔✔-classification



NLP topic modeling is a form of... - ✔✔-Dimensionality reduction



Tokenization - ✔✔-Splitting raw text into small, indivisible units for processing. Units can be words,
sentences, n-grams (n-word combos), other characters defined by regex



Stop words - ✔✔-Words that have very little semantic value



Stemming and Lemmatization - ✔✔-Cut word down to base form



Stemming- uses rough heuristics to reduce words to base



Lemmatization- uses vocabulary and morphological analysis (makes run, runs, running, and ran all the
same)



Named Entity Recognition - ✔✔-Identifies and tags named entities in text (people, places, organizations,
phone numbers, emails, etc)



aka entity extraction



Compound term extraction - ✔✔-extracting and tagging compound words or phrases in text

, Levenshtein distance - ✔✔-Minimum number of operations to get from one word to another. One way
of quantifying word similarity



Levenshtein operations - ✔✔-Deletions (delete a character)

Insertions (insert a character)

Mutation (change a character)



Corpus - ✔✔-Collection of texts



Bag of words model - ✔✔-- Simplified representation of text, where each document is recognized as a
bag of its words

- Grammar and word order are disregarded, but multiplicity is kept



Cosine similarity - ✔✔-Way to quantify the similarity between documents

1. Put each document in vector format

2. Find the cosine of the angle between the documents



Term frequency-inverse document frequency - ✔✔-(term frequency) * (inverse document frequency)



Term frequency - ✔✔-Term count/total terms



Inverse document frequency - ✔✔-- Considers how common a word is among all the documents

- Rare words get additional weight



Which classification models suffer from curse of dimensionality? - ✔✔-KNN, SVM, linear models
(linear/logisitic regression), decision trees



Distance-based models

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