Natural Language Processing with Python - Chapter 1

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Questions and Answers

What does NLP stand for?

Natural Language Processing

What is the name of the open-source library used in this book for NLP tasks?

Natural Language Toolkit (NLTK)

What does '>>>' indicate in the Python interpreter?

The Python interpreter prompt

What does the command 'from nltk.book import *' do in the Python interpreter?

<p>Loads the text of several books from the nltk.book module, including Moby Dick, Sense and Sensibility, The Book of Genesis, Inaugural Address Corpus, Chat Corpus, Monty Python and the Holy Grail, Wall Street Journal, Personals Corpus, and The Man Who Was Thursday.</p> Signup and view all the answers

What is a 'token' in NLP?

<p>A sequence of characters that is treated as a group.</p> Signup and view all the answers

What is a 'word type' in NLP?

<p>The form or spelling of a word, independent of its specific occurrences in a text.</p> Signup and view all the answers

What does the Python function 'lexical_diversity(text)' calculate?

<p>The lexical richness of a text, which is the ratio of the number of word types to the number of tokens.</p> Signup and view all the answers

What does the Python command 'len(text1)' do?

<p>Returns the number of tokens (words and punctuation marks) in the text 'text1'.</p> Signup and view all the answers

What does the Python command 'text1.count('heaven')' do?

<p>Returns the number of occurrences of the word 'heaven' in the text 'text1'.</p> Signup and view all the answers

How can we access a specific word in a list using its position?

<p>By using its index, which starts from 0 for the first word.</p> Signup and view all the answers

What does 'sent1.append("Some")' do?

<p>Adds the word 'Some' to the end of the list 'sent1'.</p> Signup and view all the answers

What does the '+' operator do when applied to lists?

<p>Concatenates two lists, creating a new list containing all the elements from the first list followed by all the elements from the second list.</p> Signup and view all the answers

What is slicing in Python?

<p>Extracting a portion of a list by specifying its starting and ending indexes. This is useful for working with sub-sections of data, like sentences within a larger text.</p> Signup and view all the answers

What is the meaning of 'sent[5:8]' in Python?

<p>It retrieves the sublist of 'sent' containing elements at indexes 5, 6, and 7.</p> Signup and view all the answers

Can you provide an example of slicing a list in Python?

<pre><code class="language-python">sent = ['word1', 'word2', 'word3', 'word4', 'word5', 'word6', 'word7', 'word8', 'word9', 'word10'] sent[2:5] # This slice extracts elements at indexes 2, 3, and 4 </code></pre> Signup and view all the answers

If a text is considered a sequence of words and punctuation, what data structure is used to represent it in Python?

<p>Lists</p> Signup and view all the answers

Flashcards

Natural Language Processing (NLP)

NLP is the manipulation of human languages by computer systems.

Python

A popular, simple programming language used in NLP.

Token

A token is a sequence of characters treated as a single unit.

Tokenization

The process of splitting text into tokens.

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Set

A collection of unique items, eliminating duplicates.

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Lexical Diversity

A measure of how many unique words are used in a text.

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Concordance

A list of occurrences of a word with context in a text.

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Context

The surrounding words or sentences where a word appears.

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Frequency Distribution

A summary of how often each word appears in a text.

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Slicing

Extracting a portion of elements from a list.

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List

A data structure in Python for storing ordered elements.

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Indexing

Accessing elements of a list by their position.

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Function

A reusable code block designed to perform a specific task.

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NLTK

Natural Language Toolkit, a library for NLP in Python.

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Corpus

A large collection of texts used for linguistic analysis.

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Part-of-Speech Tagging

Assigning word classes (e.g., noun, verb) to individual words.

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Text Generation

Creating new text based on patterns learned from existing texts.

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Named Entity Recognition (NER)

Identifying and categorizing key entities in text (e.g., names).

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Regular Expressions

Patterns used to match character combinations in strings.

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Decision Trees

A model used for classification tasks in NLP.

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Naive Bayes Classifier

A simple classifier based on applying Bayes' theorem.

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Machine Translation

Automatically translating text from one language to another.

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Sentiment Analysis

Determining emotional tone behind words in a text.

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Web Scraping

Extracting data from websites for analysis.

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Data Visualization

Using graphical representations to interpret data.

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Morphological Analysis

Analyzing the structure of words for better understanding.

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Corpus Linguistics

The study of language through real-world text corpora.

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Semantic Analysis

Understanding the meaning and interpretation of words.

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Data Preprocessing

Cleaning and organizing raw data for analysis.

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Information Retrieval

Finding relevant information within large text datasets.

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Text Classification

Categorizing text into predefined classes.

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Chatbot

An AI program designed to converse with users.

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Multilingual Processing

Handling and analyzing texts in multiple languages.

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Automated Summarization

Creating concise summaries of larger texts automatically.

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Study Notes

Natural Language Processing with Python - Chapter 1 Summary

  • Language Processing: Analyzing human language using computer programs. This can range from simple word frequency counts to more complex tasks like understanding complete sentences.

  • Python Interpreter: A program that executes Python code. Used interactively to type and run code. Shows a >>> prompt when waiting for input.

  • NLTK (Natural Language Toolkit): A Python library for NLP. Must be installed separately. The download process involves using the nltk.download() function to install data packages.

  • Texts as Lists: Python represents texts as lists of words and punctuation (tokens). Each word is an element in the list.

  • Concordance: A tool to show every occurrence of a word along with its surrounding context in a text.

  • Similar Words: A way to find words that appear in similar contexts to another given word. (Uses .similar())

  • Common Contexts: Shows contexts used by two or more words. (Uses .common_contexts())

  • Dispersion Plot: Graph visually showing word locations across a text to reveal usage patterns. This can often be visualized using libraries beyond basic Python.

  • Text Generation: Generating random text in the style of a source text by recreating patterns and word sequences found in the original.

  • Vocabulary Size: The unique words (types) in a text; distinct from the total number of words (tokens). (Uses len(set(text)) or .vocab)

  • Lexical Diversity: A measure of lexical richness in a text, calculated as the ratio of total words to unique words. It's calculated as len(text) / len(set(text)).

  • Functions: Blocks of code that perform a specific task; can be reused. Defined using def <function_name>(<parameters>):. Parameters are placeholders for the data the function acts on.

  • Arguments: Values passed to a function when it's called.

  • Lists: Ordered collections of items. Elements are accessed using indexing (e.g., myList[0] for the first element.) indexing starts with 0.

  • Slicing: Accessing sublists using slice notation (e.g., myList[2:5]).

  • Concatenation: Joining two lists into a single list. (e.g., list1 + list2)

  • Appending: Adding an item to the end of a list (myList.append(item))

  • Indexing Errors: Trying to access an element beyond the boundaries of a list results in an IndexError.

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