Natural Language Processing with Python

Natural Language Processing with Python

Steven Bird, Ewan Klein

Language: English

Pages: 504

ISBN: 0596516495

Format: PDF / Kindle (mobi) / ePub

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.

Packed with examples and exercises, Natural Language Processing with Python will help you:

  • Extract information from unstructured text, either to guess the topic or identify "named entities"
  • Analyze linguistic structure in text, including parsing and semantic analysis
  • Access popular linguistic databases, including WordNet and treebanks
  • Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence

This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

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'you', 'had', 'have', 'there', 'But', 'or', 'were', 'now', 'which', '?', 'me', 'like'] >>> fdist1['whale'] 906 >>> When we first invoke FreqDist, we pass the name of the text as an argument . We can inspect the total number of words (“outcomes”) that have been counted up — 260,819 in the case of Moby Dick. The expression keys() gives us a list of all the distinct types in the text , and we can look at the first 50 of these by slicing the list . 1.3 Computing with Language: Simple Statistics | 17.

Specified operation on the variable. The notation just described is called a “list comprehension.” This is our first example of a Python idiom, a fixed notation that we use habitually without bothering to analyze each time. Mastering such idioms is an important part of becoming a fluent Python programmer. Let’s return to the question of vocabulary size, and apply the same idiom here: >>> len(text1) 260819 >>> len(set(text1)) 19317 >>> len(set([word.lower() for word in text1])) 17231 >>> Now.

Word characters. This means that punctuation is grouped with any following letters (e.g., ’s) but that sequences of two or more punctuation characters are separated. >>> re.findall(r'\w+|\S\w*', raw) ["'When", 'I', "'M", 'a', 'Duchess', ',', "'", 'she', 'said', 'to', 'herself', ',', '(not', 'in', 'a', 'very', 'hopeful', 'tone', 'though', ')', ',', "'I", 'won', "'t", 'have', 'any', 'pepper', 'in', 'my', 'kitchen', 'AT', 'ALL', '.', 'Soup', 'does', 'very', 'well', 'without', '-', '-Maybe', 'it',.

Https:// NetworkX 4.8 A Sample of Python Libraries | 169 can be used in conjunction with Matplotlib to visualize networks, such as WordNet (the semantic network we introduced in Section 2.5). The program in Example 4-11 initializes an empty graph and then traverses the WordNet hypernym hierarchy adding edges to the graph . Notice that the traversal is recursive , applying the programming technique discussed in Section 4.7. The resulting display is shown in Figure 4-5. Example.

Copy import deepcopy), consult its documentation, and test that it makes a fresh copy of any object. 12. ◑ Initialize an n-by-m list of lists of empty strings using list multiplication, e.g., word_table = [[''] * n] * m. What happens when you set one of its values, e.g., word_table[1][2] = "hello"? Explain why this happens. Now write an expression using range() to construct a list of lists, and show that it does not have this problem. 174 | Chapter 4: Writing Structured Programs 13. ◑ Write.

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