在nltk中退避标记

Question

I am new to python coding.I want to use the UnigramTagger along with a backoff(which is in my case a RegexpTagger) and I have been struggling hard to figure out what the below error is. Appreciate any help on this.

>>> train_sents = (['@Sakshi', 'Hi', 'I', 'am', 'meeting', 'my', 'friend', 'today'])    
>>> from tag_util import patterns  
>>> from nltk.tag import RegexpTagger  
>>> re_tagger = RegexpTagger(patterns)  
>>> from nltk.tag import UnigramTagger  
>>> from tag_util import backoff_tagger  
>>> tagger = backoff_tagger(train_sents, UnigramTagger, backoff=re_tagger)

Traceback (most recent call last):  
 File "<pyshell#6>", line 1, in <module>  
    tagger = backoff_tagger(train_sents, UnigramTagger, backoff=re_tagger)  
  File "tag_util.py", line 12, in backoff_tagger  
     for cls in tagger_classes:  
TypeError: 'YAMLObjectMetaclass' object is not iterable

This is the code I have in tag_util for patterns and backoff_tagger

import re  
patterns = [  
    (r'^@\w+', 'NNP'),  
    (r'^\d+$', 'CD'),  
    (r'.*ing$', 'VBG'), # gerunds, i.e. wondering  
    (r'.*ment$', 'NN'),  
    (r'.*ful$', 'JJ'), # i.e. wonderful  
    (r'.*', 'NN')  
]  

def backoff_tagger(train_sents, tagger_classes, backoff=None):
    for cls in tagger_classes:
        backoff = cls(train_sents, backoff=backoff)
    return backoff

Answer 1

You only need to change a few things for this to work.

The error you are getting is because you cannot iterate over the class UnigramTagger . I'm not sure if you had something else in mind but just remove the for loop. Also, you need to pass UnigramTagger a list of tagged sentences represented as list s of (word, tag) tuple s - not just a list of words. Otherwise, it doesn't know how to train. Part of this might look like:

[[('@Sakshi', 'NN'), ('Hi', 'NN'),...],...[('Another', 'NN'), ('sentence', 'NN')]]

Notice here that each sentence is itself a list . Also, you can use a tagged corpus from NTLK for this (which I recommend).

Edit:

After reading your post it seems to me that you're both confused about what input/output to expect from certain functions and lacking an understanding of training in the NLP sense. I think you would greatly benefit from reading the NLTK book, starting at the beginning .

I'm glad to show you how to fix this but I don't think you'll have a complete understanding of the underlying mechanisms without some more research.

tag_util.py (based on your code)

from nltk.tag import RegexpTagger, UnigramTagger
from nltk.corpus import brown

patterns = [
    (r'^@\w+', 'NNP'),
    (r'^\d+$', 'CD'),
    (r'.*ing$', 'VBG'),
    (r'.*ment$', 'NN'),
    (r'.*ful$', 'JJ'),
    (r'.*', 'NN')
]
re_tagger = RegexpTagger(patterns)
tagger = UnigramTagger(brown.tagged_sents(), backoff=re_tagger) # train tagger

In the Python interpreter

>>> import tag_util
>>> tag_util.brown.tagged_sents()[:2]
[[('The', 'AT'), ('Fulton', 'NP-TL'), ('County', 'NN-TL'), ('Grand', 'JJ-TL'), ('Jury', 'NN-TL'), ('said', 'VBD'), ('Friday', 'NR'), ('an', 'AT'), ('investigation', 'NN'), ('of', 'IN'), ("Atlanta's", 'NP$'), ('recent', 'JJ'), ('primary', 'NN'), ('election', 'NN'), ('produced', 'VBD'), ('``', '``'), ('no', 'AT'), ('evidence', 'NN'), ("''", "''"), ('that', 'CS'), ('any', 'DTI'), ('irregularities', 'NNS'), ('took', 'VBD'), ('place', 'NN'), ('.', '.')], [('The', 'AT'), ('jury', 'NN'), ('further', 'RBR'), ('said', 'VBD'), ('in', 'IN'), ('term-end', 'NN'), ('presentments', 'NNS'), ('that', 'CS'), ('the', 'AT'), ('City', 'NN-TL'), ('Executive', 'JJ-TL'), ('Committee', 'NN-TL'), (',', ','), ('which', 'WDT'), ('had', 'HVD'), ('over-all', 'JJ'), ('charge', 'NN'), ('of', 'IN'), ('the', 'AT'), ('election', 'NN'), (',', ','), ('``', '``'), ('deserves', 'VBZ'), ('the', 'AT'), ('praise', 'NN'), ('and', 'CC'), ('thanks', 'NNS'), ('of', 'IN'), ('the', 'AT'), ('City', 'NN-TL'), ('of', 'IN-TL'), ('Atlanta', 'NP-TL'), ("''", "''"), ('for', 'IN'), ('the', 'AT'), ('manner', 'NN'), ('in', 'IN'), ('which', 'WDT'), ('the', 'AT'), ('election', 'NN'), ('was', 'BEDZ'), ('conducted', 'VBN'), ('.', '.')]]

Notice the output here. I am getting the first two sentences from the Brown corpus of tagged sentences. This is the kind of data you need to pass to a tagger as input (like the UnigramTagger) to train it. Now lets use the tagger we trained in tag_util.py .

Back to the Python interpreter

>>> tag_util.tagger.tag(['I', 'just', 'drank', 'some', 'coffee', '.'])
[('I', 'PPSS'), ('just', 'RB'), ('drank', 'VBD'), ('some', 'DTI'), ('coffee', 'NN'), ('.', '.')]

And there you have it, POS tagged words of a sentence using your approach.

Answer 2

If you are using backoff_tagger that I am thinking. UnigramTagger should be an item of a list as below:

tagger = backoff_tagger(train_sents, [UnigramTagger], backoff=re_tagger)

I hope it helps.