NLTK

Question

我想查看关于我的语料库的基本统计数据，例如 word/sentence 计数器、分布等。我有一个 tokens_corpus_reader_ready.txt，其中包含 137.000 行标记例句，格式如下：

Zur/APPRART Zeit/NN kostenlos/ADJD aber/KON auch/ADV nur/ADV 11/CARD kW./NN Zur/APPRART Zeit/NN anscheinend/ADJD kostenlos/ADJD ./$.
...

我还有一个 TaggedCorpusReader()，我有一个 describe() 方法用于：

class CSCorpusReader(TaggedCorpusReader):
  def __init__(self):
    TaggedCorpusReader.__init__(self, raw_corpus_path, 'tokens_corpus_reader_ready.txt')

    def describe(self):
    """
    Performs a single pass of the corpus and
    returns a dictionary with a variety of metrics
    concerning the state of the corpus.

    modified method from https://github.com/foxbook/atap/blob/master/snippets/ch03/reader.py
    """
    started = time.time()

    # Structures to perform counting.
    counts = nltk.FreqDist()
    tokens = nltk.FreqDist()

    # Perform single pass over paragraphs, tokenize and count
    for sent in self.sents():
        print(time.time())
        counts['sents'] += 1

        for word in self.words():
            counts['words'] += 1
            tokens[word] += 1

    return {
        'sents':  counts['sents'],
        'words':  counts['words'],
        'vocab':  len(tokens),
        'lexdiv': float(counts['words']) / float(len(tokens)),
        'secs':   time.time() - started,
    }

如果我运行在 IPython 中这样描述方法：

>> corpus = CSCorpusReader()
>> print(corpus.describe())

每句话之间有大约7秒的延迟：

1543770777.502544
1543770784.383989
1543770792.2057862
1543770798.992075
1543770805.819034
1543770812.599932
...

如果我运行同样的事情在tokens_corpus_reader_ready.txt中只用几句话输出时间是完全合理的：

1543771884.739753
1543771884.74035
1543771884.7408729
1543771884.7413561
{'sents': 4, 'words': 212, 'vocab': 42, 'lexdiv': 5.0476190476190474, 'secs': 0.002869129180908203}

这种行为从何而来，我该如何解决？

编辑 1

通过不是每次访问语料库本身而是对列表进行操作，每个句子的时间减少到大约 3 秒，但仍然很长：

    sents = list(self.sents())
    words = list(self.words())

    # Perform single pass over paragraphs, tokenize and count
    for sent in sents:
        print(time.time())
        counts['sents'] += 1

        for word in words:
            counts['words'] += 1
            tokens[word] += 1

Answer 1

这就是您的问题：对于每个句子，您使用 words() 方法阅读 整个语料库 。怪不得这么久。

for sent in self.sents():
    print(time.time())
    counts['sents'] += 1

    for word in self.words():
        counts['words'] += 1
        tokens[word] += 1

事实上，一个句子已经被标记化为单词，所以这就是你的意思：

for sent in self.sents():
    print(time.time())
    counts['sents'] += 1

    for word in sent:
        counts['words'] += 1
        tokens[word] += 1

NLTK - 大语料库的统计数据非常慢

NLTK - statistics count extremely slow with big corpus

python

performance

nlp

tagged-corpus

编辑 1