Combining a Tokenizer into a Grammar and Parser with NLTK

Question

I am making my way through the NLTK book and I can't seem to do something that would appear to be a natural first step for building a decent grammar.

My goal is to build a grammar for a particular text corpus.

(Initial question: Should I even try to start a grammar from scratch or should I start with a predefined grammar? If I should start with another grammar, which is a good one to start with for English?)

Suppose I have the following simple grammar:

simple_grammar = nltk.parse_cfg("""
S -> NP VP
PP -> P NP
NP -> Det N | Det N PP
VP -> V NP | VP PP
Det -> 'a' | 'A'
N -> 'car' | 'door'
V -> 'has'
P -> 'in' | 'for'
 """);

This grammar can parse a very simple sentence, such as:

parser = nltk.ChartParser(simple_grammar)
trees = parser.nbest_parse("A car has a door")

Now I want to extend this grammar to handle sentences with other nouns and verbs. How do I add those nouns and verbs to my grammar without manually defining them in the grammar?

For example, suppose I want to be able to parse the sentence "A car has wheels". I know that the supplied tokenizers can magically figure out which words are verbs/nouns, etc. How can I use the output of the tokenizer to tell the grammar that "wheels" is a noun?

Answer 1

You could run a POS tagger over your text and then adapt your grammar to work on POS tags instead of words.

> text = nltk.word_tokenize("A car has a door")
['A', 'car', 'has', 'a', 'door']

> tagged_text = nltk.pos_tag(text)
[('A', 'DT'), ('car', 'NN'), ('has', 'VBZ'), ('a', 'DT'), ('door', 'NN')]

> pos_tags = [pos for (token,pos) in nltk.pos_tag(text)]
['DT', 'NN', 'VBZ', 'DT', 'NN']

> simple_grammar = nltk.parse_cfg("""
  S -> NP VP
  PP -> P NP
  NP -> Det N | Det N PP
  VP -> V NP | VP PP
  Det -> 'DT'
  N -> 'NN'
  V -> 'VBZ'
  P -> 'PP'
  """)

> parser = nltk.ChartParser(simple_grammar)
> tree = parser.parse(pos_tags)