本文整理汇总了Python中nltk.corpus.util.LazyCorpusLoader.raw方法的典型用法代码示例。如果您正苦于以下问题：Python LazyCorpusLoader.raw方法的具体用法？Python LazyCorpusLoader.raw怎么用？Python LazyCorpusLoader.raw使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nltk.corpus.util.LazyCorpusLoader的用法示例。
在下文中一共展示了LazyCorpusLoader.raw方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: summarize_cisco_support_forum_texts
# 需要导入模块: from nltk.corpus.util import LazyCorpusLoader [as 别名]
# 或者: from nltk.corpus.util.LazyCorpusLoader import raw [as 别名]
def summarize_cisco_support_forum_texts():
# cisco_plain_text = LazyCorpusLoader(
# 'content', PlaintextCorpusReader, r'(?!\.).*\.txt', encoding='latin_1')
cisco_plain_text = LazyCorpusLoader(
"cisco_forum_subset", PlaintextCorpusReader, r"(?!\.).*\.txt", encoding="latin_1"
)
token_dict = {}
for article in cisco_plain_text.fileids():
token_dict[article] = cisco_plain_text.raw(article)
tfidf = TfidfVectorizer(tokenizer=tokenize_and_stem, stop_words="english", decode_error="ignore")
sys.stdout.flush()
# creates Compressed Sparse Row format numpy matrix
tdm = tfidf.fit_transform(token_dict.values())
feature_names = tfidf.get_feature_names()
# problem_statement_#1 - summarize support_forum articles automatically
for article_id in range(0, tdm.shape[0] - 2):
article_text = cisco_plain_text.raw(cisco_plain_text.fileids()[article_id])
sent_scores = []
for sentence in nltk.sent_tokenize(article_text):
score = 0
sent_tokens = tokenize_and_stem(sentence)
for token in (t for t in sent_tokens if t in feature_names):
score += tdm[article_id, feature_names.index(token)]
sent_scores.append((score / len(sent_tokens), sentence))
summary_length = int(math.ceil(len(sent_scores) / 5))
sent_scores.sort(key=lambda sent: sent[0])
print "\n*** SUMMARY ***"
for summary_sentence in sent_scores[:summary_length]:
print summary_sentence[1]
print "\n*** ORIGINAL ***"
print article_text
# problem_statement_#2 - automatically categorize forum posts by tags into various groups
reduce_dimensionality_and_cluster_docs(tfidf, tdm, num_features=200)
# problem_statement_#3 - find similar documents to a current document (that user is reading) automatically
# eg - quora: find similar questions, find similar answers
cosine_similarity(tdm[0:1], tdm)
"""
output looks like this
array([[ 1. , 0.22185251, 0.0215558 , 0.03805012, 0.04796646,
0.05069365, 0.05507056, 0.03374501, 0.03643342, 0.05308392,
0.06002623, 0.0298806 , 0.04177088, 0.0844478 , 0.07951179,
0.02822186, 0.03036787, 0.11022385, 0.0535391 , 0.10009412,
0.07432719, 0.03753424, 0.06596462, 0.01256566, 0.02135591,
0.13931643, 0.03062681, 0.02595649, 0.04897851, 0.06276997,
0.03173952, 0.01822134, 0.04043555, 0.06629454, 0.05436211,
0.0549144 , 0.04400169, 0.05157118, 0.05409632, 0.09541703,
0.02473209, 0.05646599, 0.05728387, 0.04672681, 0.04519217,
0.04126276, 0.06289187, 0.03116767, 0.04828476, 0.04745193,
0.01404426, 0.04201325, 0.023492 , 0.07138136, 0.03778315,
0.03677206, 0.02553581]])
The first document is compared to the rest, with the most similar to it being itself with score of 1, next most similar to it is document with score 0.22185251
"""
cosine_similarities = linear_kernel(tdm[0:1], tdm).flatten()
# mapping back to document_name space
related_docs_indices = cosine_similarities.argsort()
"""
document_ids
array([23, 50, 31, 24, 2, 52, 40, 56, 27, 15, 11, 16, 26, 47, 30, 7, 8,
55, 21, 54, 3, 32, 45, 12, 51, 36, 44, 43, 49, 4, 48, 28, 5, 37,
9, 18, 38, 34, 35, 6, 41, 42, 10, 29, 46, 22, 33, 53, 20, 14, 13,
39, 19, 17, 25, 1, 0])
docs 0 and 1 are very similar which are the following posts (last 2 array elements above when sorted)
https://supportforums.cisco.com/discussion/11469881/aniserver-failed-run-lms-40
and
supportforums.cisco.com/discussion/11469606/eos-lms-31-support-quest
"""
cosine_similarities[related_docs_indices]
for key, value in token_dict.iteritems():
print key, value
# find the actual posts which are the most similar
tfidf.inverse_transform(tdm)[0]
tfidf.inverse_transform(tdm)[1]