NLTK on the Raspberry PI

If you haven’t heard of it yet, the Raspberry Pi is a $25/$35 barebones computer intended to excite kids with programming and hardware projects. It is very much modeled on the British experience of home computing in the early 1980s and even has a “Model A” and a “Model B” in homage to the BBC ...

Calculating Word Frequency Tables 2

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.

Segmenting Words and Sentences 1

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 ...

Extracting Noun Phrases from Parsed Trees 6

Following on from my previous post about NLTK Trees, here is a short Python function to extract phrases from an NLTK Tree structure.

NLTK Trees

A number of NLTK functions work with Tree objects. For example, part of speech tagging and chunking classifiers, naturally return trees. Sentence manipulation functions also work with trees. Although Natural Language Processing with Python (Bird et al) includes a couple of pages about NLTK’s Tree module, coverage is generally sparse. The online documentation actually contains ...

Support for SciPy in NLTK’s Maximum Entropy methods

Recently I have been working with the Maximum Entropy classifiers in NLTK. Maximum entropy models are similar to the well known Naive Bayes models but they allow for independence between the features – i.e. they are not “naive”. SciPy has had some problems with its Maximum Entropy code, and v0.8 must be used. v0.9 crashes ...