Comments (2)
@shuizhonghaitong
您好, 抱歉没能及时回复. 我们用的不是IR里面标准的TF-IDF定义. 这里我们主要是希望能够度量语料库中每个词的重要性, 然后用于后续的关键词抽取等模块. 我们把每一首诗看做一个文档,然后可以得到每个词的IDF值 log (D/(d_wi+1)), 但是我们发现仅用IDF的话, 很多低频词(经常是出现分词错误的n-gram)的IDF会很高, 所以又乘上了每个词在整个语料上的频次, f_wi, 来调和一下. 所以最终用 weight(wi) = log(f_wi+1) * log (D/(d_wi+1)) 来度量每个词的重要性. 由于形式上还是词频乘以逆文档频率的形式, 类似TF-IDF, 所以我们还是称之为TF-IDF.
现在我们所有诗歌相关的模型数据都已经转到九歌的github上维护了: https://github.com/THUNLP-AIPoet
如果您还有其他问题, 可以到上述github里面对应的repo下面开个issue, 那边我们会维护和check得频繁一些, 也能够更快回复.
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非常感谢您的详细回复跟我猜想的差不多
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