By Jacob Perkins
This booklet is meant for Python programmers drawn to studying the best way to do ordinary language processing. possibly you’ve discovered the boundaries of normal expressions the difficult means, or you’ve discovered that human language can't be deterministically parsed like a working laptop or computer language. probably you've got extra textual content than you recognize what to do with, and wish automatic how you can research and constitution that textual content. This Cookbook will help you educate and use statistical language types to method textual content in ways in which are essentially most unlikely with typical programming instruments. A simple wisdom of Python and the elemental textual content processing recommendations is anticipated. a few adventure with average expressions can also be precious.
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Likelihood_ratio, 4) [('long', 'term', 'relationship')] Now, we don't know whether people are looking for a long-term relationship or not, but clearly it's an important topic. In addition to the stopword filter, I also applied a frequency filter, which removed any trigrams that occurred less than three times. This is why only one result was returned when we asked for four because there was only one result that occurred more than two times. 26 Chapter 1 Scoring functions There are many more scoring functions available besides likelihood_ratio().
In replace_negations(), we look through a tokenized sentence for the word not. If not is found, then we try to find an antonym for the next word using replace(). If we find an antonym, then it is appended to the list of words, replacing not and the original word. All other words are appended as is, resulting in a tokenized sentence with unambiguous negations replaced by their antonyms. There's more... As unambiguous antonyms aren't very common in WordNet, you might want to create a custom antonym mapping in the same way we did for synonyms.
Org/wiki/PyYAML. path. Creating a wordlist corpus The WordListCorpusReader class is one of the simplest CorpusReader classes. It provides access to a file containing a list of words, one word per line. In fact, you've already used it when we used the stopwords corpus in Chapter 1, Tokenizing Text and WordNet Basics, in the Filtering stopwords in a tokenized sentence and Discovering word collocations recipes. Getting ready We need to start by creating a wordlist file. This could be a single column CSV file, or just a normal text file with one word per line.