Text Analytics and NLP Overview

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Questions and Answers

What is an example of a tri-gram?

new space travel

he walked

to the moon (correct)

I have

Which technique is used to consolidate words with the same root?

Frequency analysis

N-grams

Tokenization

Stemming (correct)

What do stop words refer to in frequency analysis?

Words that should always be included in analysis

Commonly used words that add little meaning (correct)

Technical terms specific to a subject

Words that indicate the main subject

What does term frequency - inverse document frequency (TF-IDF) measure?

The relevance of a term across multiple documents (B) Signup and view all the answers

In text classification using logistic regression, what is a common application?

Sentiment analysis (B) Signup and view all the answers

Why might simple frequency analysis be ineffective across multiple documents?

It does not differentiate between documents (B) Signup and view all the answers

What is the primary focus of the most common words in a text?

They highlight the text's main subject (D) Signup and view all the answers

Which option accurately describes n-grams?

Phrases formed by grouping words together (B) Signup and view all the answers

What is the first step in analyzing a corpus?

Tokenization (A) Signup and view all the answers

What is the purpose of text normalization in NLP?

To remove punctuation and standardize word casing (D) Signup and view all the answers

Which of the following is an example of a stop word?

It (B) Signup and view all the answers

Why is tokenization critical when analyzing text?

It breaks text into smaller units for further processing (B) Signup and view all the answers

What could be a consequence of not using text normalization?

Analysis may be skewed due to inconsistent text format (A) Signup and view all the answers

In the phrase 'Mr Banks has worked in many banks.', how might text normalization affect the analysis?

It would merge both instances of 'banks' into one token (D) Signup and view all the answers

What is the primary goal of stop word removal in the context of text analysis?

To focus on more meaningful words in the analysis (A) Signup and view all the answers

Which statement accurately reflects a technique related to statistical analysis of text?

It often relies on the frequency of token appearances. (D) Signup and view all the answers

What do the labeled restaurant reviews indicate about the sentiment of the review?

Words like 'terrible' and 'slow' generally lead to a sentiment of 0. (A) Signup and view all the answers

What is an embedding in the context of natural language processing?

A multi-valued array representing language tokens in a high-dimensional space. (A) Signup and view all the answers

How does the location of a token in embedding space relate to its meaning?

Closer tokens are considered to be semantically related. (B) Signup and view all the answers

What is one of the key principles behind training classification models for sentiment analysis?

The model uses tokenized text as features for prediction. (A) Signup and view all the answers

What is a characteristic of modern language models used in natural language processing?

They can produce varied predictions based on different embeddings. (A) Signup and view all the answers

In the provided token examples, which two tokens are closest to each other in the embedding space?

'cat' and 'bark' (D) Signup and view all the answers

What does the term 'token' refer to in the context of language processing?

Any sequence of characters that can represent words or phrases. (B) Signup and view all the answers

Why is it important for embeddings to be in high-dimensional space?

It provides more granularity in representing the relationships among tokens. (D) Signup and view all the answers

Study Notes

Text Analytics and NLP

Text analytics uses statistical analysis of a corpus of text to infer semantic meaning.
The first step in analyzing a corpus is to break it down into tokens.
Tokens are distinct words or parts of words in the text.
Tokenization can include text normalization, stop word removal, n-grams, and stemming.
Text normalization removes punctuation and changes words to lowercase.
Stop words are common words like "the", "a", and "it" that add little semantic meaning.
N-grams are multi-term phrases, like "I have".
Stemming consolidates words with the same root, like "power", "powered", and "powerful".
Frequency analysis counts the number of occurrences of each token, which can reveal the main subject of a text corpus.
TF-IDF is a technique to determine the relevance of words in a document, considering their frequency in the document and the entire corpus.

Machine Learning for Classification

Classification algorithms are used to train machine learning models to classify text based on categories.
A common application is sentiment analysis, classifying text as positive or negative.
Training data involves labeled text with corresponding categories, allowing the model to learn the relationship between tokens and categories.

Semantic Language Models

These models encode language tokens as vectors known as embeddings.
Embeddings represent tokens in a multidimensional space, where the closer tokens are, the more semantically related they are.
Language models use these embeddings to capture complex semantic relationships between words.
Embeddings have many dimensions and are calculated using different methods, which can affect model predictions.

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Description

This quiz covers key concepts in text analytics and natural language processing, including tokenization, text normalization, and frequency analysis. You'll learn about techniques like stop word removal and TF-IDF that help in understanding and classifying text data. Test your knowledge of these foundational topics in NLP.