From the perspective of Baidu search engine optimization, if the word segmentation technology can be used properly, you can optimize your website to a higher level. In fact, the principle of word segmentation is very simple, that is, when the user enters the query keyword, it can match the user accurately. The output result is also the user-friendly experience that Baidu is pursuing. The editor of the super ranking system organizes and publishes it.
If you can master Baidu’s word segmentation technology, you can achieve website keyword positioning, and you can list long-tail keywords, which will drive better website optimization and attract more traffic. Baidu word segmentation technology is more advanced than Google word segmentation The reason is that Baidu has a huge vocabulary, including names, place names, business names, etc., as well as forward matching and reverse matching to meet users’ search needs with a shorter path.
Baidu word segmentation mainly satisfies the search engine’s capture of words in terms of word meaning, words, and word frequency. The specific word segmentation principles are divided into these three parts:
- String matching word segmentation method
Subdivided into forward matching method, reverse matching method, short path word segmentation and so on.
- Forward matching method
The forward matching method is mainly based on the way we write for a long time, to segment a word or a sentence from left to right, for example: “a student is studying in the classroom”, the forward matching method of this sentence is one, student, Now, classroom, upper, self-study, mainly adopt the matching method from left to right.
- Reverse matching method
The reverse matching method is just the opposite of the forward matching method. For example, “a student is studying in the classroom” mainly distinguishes students from right to left using the reverse matching method.
- Shorter path word segmentation
In fact, the number of words that need to be separated in a paragraph is relatively small. As far as possible, a sentence is divided into several words to distinguish. There are also special cases, which is a combination of forward matching, reverse matching, and short path matching. Methods, for example, the combination of forward maximum matching and reverse maximum matching is called the two-way maximum matching method.
- Word meaning segmentation method
The word-sense word segmentation method is to use a machine language to judge the word segmentation, perform syntactic and semantic analysis, and use grammatical and semantic information to make judgments and deal with ambiguity. At present, such a method is not mature in Baidu.
- Statistical analysis methods
Statistical analysis is mainly carried out under manual labeling and statistical features. For Chinese, a model is established. In the word segmentation stage, the model is used to calculate the probability of word segmentation. The result of the probability can be used as the final bargaining chip. The more common sequence models include HMM and CRF.
The advantage is that it can handle the ambiguity and the inability to register words, and the effect is better than the string matching effect.
The disadvantage is that a lot of manual labeling may be required, and the speed will be relatively slow.
Because the adjacent words appear more frequently at the same time, the more likely they are to form a word, so the probability of occurrence of the adjacent part of the word and word can well reflect the credibility of the word.
It is also possible to count the combination frequency of each word appearing on the edge of the corpus, to estimate their common information, to define this information, and to calculate the probability of adjacent occurrence of this word.
In the process of Baidu word segmentation analysis, whether it is title TItle word segmentation or home page related keyword setting, we can not use any keyword in Baidu search at will, because you will find that the home page title can use Baidu search engine to remove the relevant key The word ranks high.