Python自然语言处理 7.3 Training Classifier-Based Chunkers 中，algorithm=‘MEGAM‘ 的示例报错

执行示例代码

def npchunk_features(sentence, i, history):
    word, pos = sentence[i]
    return {"pos": pos}

class ConsecutiveNPChunkTagger(nltk.TaggerI):

    def __init__(self, train_sents):
        train_set = []
        for tagged_sent in train_sents:
            untagged_sent = nltk.tag.untag(tagged_sent)
            history = []
            for i, (word, tag) in enumerate(tagged_sent):
                featureset = npchunk_features(untagged_sent, i, history)
                train_set.append((featureset, tag))
                history.append(tag)
        # self.classifier = nltk.NaiveBayesClassifier.train(train_set)
        self.classifier = nltk.MaxentClassifier.train(
            train_set, algorithm='MEGAM', trace=0)  #

    def tag(self, sentence):
        history = []
        for i, word in enumerate(sentence):
            featureset = npchunk_features(sentence, i, history)
            tag = self.classifier.classify(featureset)
            history.append(tag)
        return zip(sentence, history)

class ConsecutiveNPChunker(nltk.ChunkParserI):
    def __init__(self, train_sents):
        tagged_sents = [[((w,t),c) for (w,t,c) in
                         nltk.chunk.tree2conlltags(sent)]
                        for sent in train_sents]
        self.tagger = ConsecutiveNPChunkTagger(tagged_sents)

    def parse(self, sentence):
        tagged_sents = self.tagger.tag(sentence)
        conlltags = [(w, t, c) for ((w, t), c) in tagged_sents]
        return nltk.chunk.conlltags2tree(conlltags)

test_sents = conll2000.chunked_sents('test.txt', chunk_types=['NP'])
train_sents = conll2000.chunked_sents('train.txt', chunk_types=['NP'])
chunker = ConsecutiveNPChunker(train_sents)
print(chunker.evaluate(test_sents))

报错

Traceback (most recent call last):
  File "E:/Python Practice/NLP/Chapter7.py", line 225, in <module>
    chunker = ConsecutiveNPChunker(train_sents)
  File "E:/Python Practice/NLP/Chapter7.py", line 216, in __init__
    self.tagger = ConsecutiveNPChunkTagger(tagged_sents)
  File "E:/Python Practice/NLP/Chapter7.py", line 201, in __init__
    train_set, algorithm='MEGAM', trace=0)  #
  File "D:\Anaconda3\lib\site-packages\nltk\classify\maxent.py", line 335, in train
    train_toks, trace, encoding, labels, gaussian_prior_sigma, **cutoffs
  File "D:\Anaconda3\lib\site-packages\nltk\classify\maxent.py", line 1483, in train_maxent_classifier_with_megam
    stdout = call_megam(options)
  File "D:\Anaconda3\lib\site-packages\nltk\classify\megam.py", line 168, in call_megam
    config_megam()
  File "D:\Anaconda3\lib\site-packages\nltk\classify\megam.py", line 57, in config_megam
    url="http://www.umiacs.umd.edu/~hal/megam/index.html",
  File "D:\Anaconda3\lib\site-packages\nltk\internals.py", line 690, in find_binary
    name, path_to_bin, env_vars, searchpath, binary_names, url, verbose
  File "D:\Anaconda3\lib\site-packages\nltk\internals.py", line 674, in find_binary_iter
    path_to_bin or name, env_vars, searchpath, binary_names, url, verbose
  File "D:\Anaconda3\lib\site-packages\nltk\internals.py", line 632, in find_file_iter
    raise LookupError("\n\n%s\n%s\n%s" % (div, msg, div))
LookupError: 

===========================================================================
NLTK was unable to find the megam file!
Use software specific configuration paramaters or set the MEGAM environment variable.

  For more information on megam, see:
    <http://www.umiacs.umd.edu/~hal/megam/index.html>
===========================================================================

通过StackOverflow，在这里下载MEGAM源文件Source: megam_src.tgz，下载之后解压，然后在代码中（程序的开始）为MEGAM添加环境变量

import os
os.environ["MEGAM"] = 'D:\Anaconda3\Lib\site-packages\MEGAM\megam-64'

再次运行，报错

  File "E:/Python Practice/NLP/Chapter7.py", line 204, in __init__
    train_set, algorithm='MEGAM', trace=0)  #
  File "D:\Anaconda3\lib\site-packages\nltk\classify\maxent.py", line 335, in train
    train_toks, trace, encoding, labels, gaussian_prior_sigma, **cutoffs
  File "D:\Anaconda3\lib\site-packages\nltk\classify\maxent.py", line 1483, in train_maxent_classifier_with_megam
    stdout = call_megam(options)
  File "D:\Anaconda3\lib\site-packages\nltk\classify\megam.py", line 172, in call_megam
    p = subprocess.Popen(cmd, stdout=subprocess.PIPE)
  File "D:\Anaconda3\lib\subprocess.py", line 800, in __init__
    restore_signals, start_new_session)
  File "D:\Anaconda3\lib\subprocess.py", line 1207, in _execute_child
    startupinfo)
OSError: [WinError 193] %1 is not a valid Win32 application

该错误原因是python版本是64位，调用的库是32位的，所以解决方法便是安装32位的python。在官网下载一个32位的python，这里选择 python3.7.7 Windows x86 executable installer 安装成功，更换解释器之后还是报同样的错误。。。

由于本人使用了Anaconda，又在Anaconda官网安装了一个32位的Anaconda，依旧报错。。。

有时间再来更新

Python自然语言处理 7.3 Training Classifier-Based Chunkers 中，algorithm=‘MEGAM‘ 的示例报错

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