【TF-IDF介绍】

gaojingsong

浏览: 1197175 次
性别:
来自: 深圳

最近访客更多访客>>

zah5897

xckouy

lengyun3566

Z865785437029

博主相关

博客

微博

相册

留言

关于我

文章分类

社区版块

存档分类

博客分类：

自然语言处理

TF-IDF介绍

TF-IDF（term frequency–inverse document frequency）是一种用于资讯检索与资讯探勘的常用加权技术, TFIDF的主要思想是：如果某个词或短语在一篇文章中出现的频率TF高，并且在其他文章中很少出现，则认为此词或者短语具有很好的类别区分能力，适合用来分类。TFIDF实际上是：TF * IDF，TF词频(Term Frequency)，IDF反文档频率(Inverse Document Frequency)。TF表示词条在文档d中出现的频率。

In information retrieval, tf–idf, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.[1] It is often used as a weighting factor in information retrieval, text mining, and user modeling. The tf-idf value increases proportionally to the number of times a word appears in the document, but is offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words appear more frequently in general. Nowadays, tf-idf is one of the most popular term-weighting schemes. For instance, 83% of text-based recommender systems in the domain of digital libraries use tf-idf[2].

Variations of the tf–idf weighting scheme are often used by search engines as a central tool in scoring and ranking a document's relevance given a user query. tf–idf can be successfully used for stop-words filtering in various subject fields including text summarization and classification.

One of the simplest ranking functions is computed by summing the tf–idf for each query term; many more sophisticated ranking functions are variants of this simple model.

Term frequency

Suppose we have a set of English text documents and wish to determine which document is most relevant to the query "the brown cow". A simple way to start out is by eliminating documents that do not contain all three words "the", "brown", and "cow", but this still leaves many documents. To further distinguish them, we might count the number of times each term occurs in each document and sum them all together; the number of times a term occurs in a document is called its term frequency.

The first form of term weighting is due to Hans Peter Luhn (1957) and is based on the Luhn Assumption:

The weight of a term that occurs in a document is simply proportional to the term frequency.

Inverse document frequency

Because the term "the" is so common, term frequency will tend to incorrectly emphasize documents which happen to use the word "the" more frequently, without giving enough weight to the more meaningful terms "brown" and "cow". The term "the" is not a good keyword to distinguish relevant and non-relevant documents and terms, unlike the less common words "brown" and "cow". Hence an inverse document frequency factor is incorporated which diminishes the weight of terms that occur very frequently in the document set and increases the weight of terms that occur rarely.

Karen Spärck Jones (1972) conceived a statistical interpretation of term specificity called Inverse Document Frequency (IDF), which became a cornerstone of term weighting:

The specificity of a term can be quantified as an inverse function of the number of documents in which it occurs.

IDF的主要思想是：如果包含词条t的文档越少，也就是n越小，IDF越大，则说明词条t具有很好的类别区分能力。如果某一类文档C中包含词条t的文档数为m，而其它类包含t的文档总数为k，显然所有包含t的文档数n=m + k，当m大的时候，n也大，按照IDF公式得到的IDF的值会小，就说明该词条t类别区分能力不强。但是实际上，如果一个词条在一个类的文档中频繁出现，则说明该词条能够很好代表这个类的文本的特征，这样的词条应该给它们赋予较高的权重，并选来作为该类文本的特征词以区别与其它类文档。这就是IDF的不足之处.

案例：假如一篇文件的总词语数是100个，而词语“母牛”出现了3次，那么“母牛”一词在该文件中的词频就是3/100=0.03。一个计算文件频率 (DF) 的方法是测定有多少份文件出现过“母牛”一词，然后除以文件集里包含的文件总数。所以，如果“母牛”一词在1,000份文件出现过，而文件总数是10,000,000份的话，其逆向文件频率就是 lg(10,000,000 / 1,000)=4。最后的TF-IDF的分数为0.03 * 4=0.12。

IDF的主要思想是：如果包含词条t的文档越少，也就是n越小，IDF越大，则说明词条t具有很好的类别区分能力。如果某一类文档C中包含词条t的文档数为m，而其它类包含t的文档总数为k，显然所有包含t的文档数n=m+k，当m大的时候，n也大，按照IDF公式得到的IDF的值会小，就说明该词条t类别区分能力不强。但是实际上，如果一个词条在一个类的文档中频繁出现，则说明该词条能够很好代表这个类的文本的特征，这样的词条应该给它们赋予较高的权重，并选来作为该类文本的特征词以区别与其它类文档。这就是IDF的不足之处. 在一份给定的文件里，词频（term frequency，TF）指的是某一个给定的词语在该文件中出现的频率。这个数字是对词数(term count)的归一化，以防止它偏向长的文件。（同一个词语在长文件里可能会比短文件有更高的词数，而不管该词语重要与否。）对于在某一特定文件里的词语 $t_{i}$ 来说，它的重要性可表示为：