By Steven Bird, Ewan Klein
This publication deals a hugely available advent to ordinary language processing, the sector that helps quite a few language applied sciences, from predictive textual content and e-mail filtering to computerized summarization and translation. With it, you'll the way to write Python courses that paintings with huge collections of unstructured textual content. You'll entry richly annotated datasets utilizing a entire diversity of linguistic info constructions, and you'll comprehend the most algorithms for reading the content material and constitution of written communication.
Packed with examples and routines, Natural Language Processing with Python may help you:
Extract details from unstructured textual content, both to bet the subject or determine "named entities"
Analyze linguistic constitution in textual content, together with parsing and semantic analysis
Access renowned linguistic databases, together with WordNet and treebanks
Integrate concepts drawn from fields as various as linguistics and synthetic intelligence
This e-book can assist you achieve functional abilities in traditional language processing utilizing the Python programming language and the average Language Toolkit (NLTK) open resource library. If you're drawn to constructing internet purposes, reading multilingual information assets, or documenting endangered languages -- or if you're easily curious to have a programmer's viewpoint on how human language works -- you'll locate Natural Language Processing with Python either interesting and immensely helpful.
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Additional resources for Natural Language Processing with Python
My_var. Be careful not to insert a hyphen instead of an underscore: my-var is wrong, since Python interprets the - as a minus sign. Strings Some of the methods we used to access the elements of a list also work with individual words, or strings. For example, we can assign a string to a variable , index a string , and slice a string . split() ['Monty', 'Python'] >>> We will come back to the topic of strings in Chapter 3. For the time being, we have two important building blocks—lists and strings—and are ready to get back to some language analysis.
8. ○ Consider the following Python expression: len(set(text4)). State the purpose of this expression. Describe the two steps involved in performing this computation. 9. 2 on lists and strings. a. , my_string = 'My String' (but put something more interesting in the string). Print the contents of this variable in two ways, first by simply typing the variable name and pressing Enter, then by using the print statement. b. , my_string * 3. Notice that the strings are joined together without any spaces.
Items of text3 with the command: set(text3). When you do this, many screens of words will fly past. ] >>> len(set(text3)) 2789 >>> By wrapping sorted() around the Python expression set(text3) , we obtain a sorted list of vocabulary items, beginning with various punctuation symbols and continuing with words starting with A. All capitalized words precede lowercase words. We discover the size of the vocabulary indirectly, by asking for the number of items in the set, and again we can use len to obtain this number .