aimeelee77 / keyword_extraction Goto Github PK
View Code? Open in Web Editor NEW利用Python实现中文文本关键词抽取，分别采用TF-IDF、TextRank、Word2Vec词聚类三种方法。
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stopkey = [w.strip() for w in codecs.open('data/stopWord.txt', 'r').readlines()]
python3下，需要修改为：
stopkey = [w.strip() for w in codecs.open('data/stopWord.txt', 'r', encoding='UTF-8').readlines()]
关于乱码问题
大佬您吼~~我按照您word2vec+聚类的代码跑了一遍，但是输出的csv文件的关键词一直存在着编码的问题，key那一列对应的是b"b'\xe8\x8d\xaf\xe7\x89\xa9' b'\xe5\x..这种格式，不知道您有没有好的建议可以修改？
IOError: [Errno 2] No such file or directory: 'wiki.zh.text.vector'
下载完 直接运行python keyextract_word2vec_1.py 报“IOError: [Errno 2] No such file or directory: 'wiki.zh.text.vector'” 怎么解决?
关于K-means聚类报错
IndexError: index 1 is out of bounds for axis 0 with size 1，想问下如何解决
关于tf-idf模型候选数据集过大，是否越大越好？
我数据集情况，三类标签，每个类别1W条以上数据。我需要每个标签提取20-50个关键词。
由于数据集多大，jieba分词速度慢，占内存大外，用于训练tf-idf时候出现很多负例（无效关键词），影响模型效果。现在自己做法是限制词频。但想问数据集是否过大需要调整么？该怎么做？
聚类速度怎么样
请问可以分享一下代码吗
楼主您好！请问可以分享一下代码吗
词的维度
请问# 词向量写入csv文件，每个词400维是哪个代码设置的？
关键词提取的效率如何？
有针对这些算法进行提取效率的对比吗？效率如何
K-means 簇设置为1，不合适吧？这样的话所有的点都属于同一个簇，模型不会迭代，聚类中心就是计算所有点平均值得到的，这个中心一直不会变，所以将簇设为1采用聚类算法没有意义
关于TF-IDF导出乱码问题
用的是Python3，但在导出CSV时，关键词是'b'\xe7\x94\等，想问一下如何转换成汉字？
几乎所有写关键词教程的人，都没回答一个问题，那就是怎样衡量算法的准确性？
或者怎样衡量算法抽取的关键词就是文本的关键？
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