I'm currently using NLTK for language processing, but I have encountered a problem of sentence tokenizing.
Here's the problem:
Assume I have a sentence: "Fig. 2 shows a U.S.A. map."
When I use punkt tokenizer, my code looks like this:
from nltk.tokenize.punkt import PunktSentenceTokenizer, PunktParameters
punkt_param = PunktParameters()
abbreviation = ['U.S.A', 'fig']
punkt_param.abbrev_types = set(abbreviation)
tokenizer = PunktSentenceTokenizer(punkt_param)
tokenizer.tokenize('Fig. 2 shows a U.S.A. map.')
It returns this:
['Fig. 2 shows a U.S.A.', 'map.']
The tokenizer can't detect the abbreviation "U.S.A.", but it worked on "fig".
Now when I use the default tokenizer NLTK provides:
import nltk
nltk.tokenize.sent_tokenize('Fig. 2 shows a U.S.A. map.')
This time I get:
['Fig.', '2 shows a U.S.A. map.']
It recognizes the more common "U.S.A." but fails to see "fig"!
How can I combine these two methods? I want to use default abbreviation choices as well as adding my own abbreviations.
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