难以转换列表：'str' object 没有属性 'items'

Question

I am trying to create a classifier using NLTK, however, I believe that I have a problem in the format of my data that I cannot get over.

My data looks like this:

data = [("TEXT 1", 'no'), ("TEXT 2", 'yes'), ("TEXT 3", 'no'), ("TEXT 4", 'no'), ("TEXT 5", 'yes')]

Then, I run the following code:

 import nltk
    from nltk.classify import maxent
    
    classifier = maxent.MaxentClassifier.train(data, bernoulli=False, max_iter=10)

But, unfortunately I have the following error. What does this error consist of and how do I overcome it?

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-93-e1b29adbeebb> in <module>
----> 1 classifier = maxent.MaxentClassifier.train(data, bernoulli=False, max_iter=10)

/usr/local/lib/python3.8/dist-packages/nltk/classify/maxent.py in train(cls, train_toks, algorithm, trace, encoding, labels, gaussian_prior_sigma, **cutoffs)
    324         algorithm = algorithm.lower()
    325         if algorithm == "iis":
--> 326             return train_maxent_classifier_with_iis(
    327                 train_toks, trace, encoding, labels, **cutoffs
    328             )

/usr/local/lib/python3.8/dist-packages/nltk/classify/maxent.py in train_maxent_classifier_with_iis(train_toks, trace, encoding, labels, **cutoffs)
   1175     # Construct an encoding from the training data.
   1176     if encoding is None:
-> 1177         encoding = BinaryMaxentFeatureEncoding.train(train_toks, labels=labels)
   1178 
   1179     # Count how many times each feature occurs in the training data.

/usr/local/lib/python3.8/dist-packages/nltk/classify/maxent.py in train(cls, train_toks, count_cutoff, labels, **options)
    665 
    666             # Record each of the features.
--> 667             for (fname, fval) in tok.items():
    668 
    669                 # If a count cutoff is given, then only add a joint

AttributeError: 'str' object has no attribute 'items'

Answer 1

From the documentation:

train(train_toks, algorithm=None, trace=3, encoding=None, labels=None, gaussian_prior_sigma=0, **cutoffs)

Train Docs

Parameters train_toks (list) – Training data, represented as a list of pairs, the first member of which is a featureset , and the second of which is a classification label.

Your tuples need to have the first element be a dict that "map[s] strings to either numbers, booleans or strings" then you need to have your second element be the classification label.

from nltk.classify import maxent

data = [({"TEXT 1": 'no'}, "Label")]
classifier = maxent.MaxentClassifier.train(data, bernoulli=False, max_iter=10)