Previously, I showed you how to create N-Gram frequency tables from large text datasets. Unfortunately, when used on very large datasets such as the English language Wikipedia and Gutenberg corpora, memory limitations limited these scripts to unigrams. Here, I show you how to use the BerkeleyDB database to create N-gram tables of these large datasets.
NLTK 2.0 has officially been released as “v2.0.1”., and can be downloaded here: http://pypi.python.org/pypi/nltk/2.0.1 NLTK 2.0 was previously released as a “Release Candidate” – this is the first official release.
“Foundations of Statistical Natural Language Processing” by Christopher D. Manning and Hinrich Schutze has a relatively old publication date of 1999, but do not let this deter you from reading this useful book. This book continues to be an important foundation text in a fast moving field.
The Word Frequency Table scripts can be easily expanded to calculate N-Gram frequency tables. This post explains how.
As well as using the Gutenberg Corpus, it is possible to create a word frequency table for the English text of the Wikipedia encyclopedia.
Following on from the previous article about scanning text files for word statistics, I shall extend this to use real large corpora. First we shall use this script to create statistics for the entire Gutenberg English language corpus. Next I shall do the same with the entire English language Wikipedia.
Now that we can segment words and sentences, it is possible to produce word and tuple frequency tables. Here I show you how to create a word frequency table for a large collection of text files.
Even simple NLP tasks such as tokenizing words and segmenting sentences can have their complexities. Punctuation characters could be used to segment sentences, but this requires the punctuation marks to be treated as separate tokens. This would result in abbreviations being split into separate words and sentences. This post uses a classification approach to create ...
Although “Natural Language Understanding” by James Allen is an older book, it still contains some useful content presented in a readable form. Although more modern books take a more statistical approach, this book has good, clear presentations of formal grammar, logic, and conversation agent topics.
Following on from my previous post about NLTK Trees, here is a short Python function to extract phrases from an NLTK Tree structure.