采用Bigram 方法[4]进行字符串相似性度量，设有字符串
属性r1 和r2，令Gr1 和Gr2 分别表示字符串的Bigram 集合，
则2 个字符串的相似性分数计算公式为
http://en.wikipedia.org/wiki/Bigram
From Wikipedia, the free encyclopedia
Bigrams
or digrams
are groups of two written letters, two syllables, or two words, and are
very commonly used as the basis for simple statistical analysis of
text. They are used in one of the most successful language models
for speech recognition
.[
1]
They are a special case of N-gram
.
Gappy bigrams
or skipping bigrams
are word pairs which allow gaps (perhaps avoiding connecting words, or allowing some simulation of dependencies, as in a dependency grammar
).
Head word bigrams
are gappy bigrams with an explicit dependency relationship.
The term is also used in cryptography
, where bigram frequency attacks
have sometimes been used to attempt to solve cryptograms
. See frequency analysis
.
Bigrams help provide the conditional probability of a word given the preceding word, when the relation of the conditional probability
is applied:
That is, the probability P
()
of a word W
n
given the preceding word W
n
− 1
is equal to the probability of their bigram, or the co-occurrence of the two words P
(W
n
− 1
,W
n
)
, divided by the probability of the preceding word.