A method for establishing a word segmentation model, a word segmentation method and a device thereof
A word segmentation and model technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of information loss, reduce word segmentation accuracy, etc., and achieve the effect of improving accuracy and expanding dimensions
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Publication Date
- 2016-06-29
Smart Images
Figure 1 Figure 2 Figure 3
Abstract
Description
Technical field
[0001] The invention relates to the technical field of natural language processing, in particular to a method for establishing a word segmentation model, a word segmentation method and a device thereof.
Background technique
[0002] With the widespread use of the Internet, more and more texts and information are disseminated through the Internet. In order to retrieve and mine valuable content from these texts and information, natural language processing is an indispensable technology, and word segmentation is Fundamental work in natural language processing.
[0003] In the prior art, word segmentation mainly includes rule-based word segmentation and statistics-based word segmentation. Rule-based word segmentation includes forward maximum matching, reverse maximum matching, two-way maximum matching, shortest segmentation segmentation, segmentation based on rule sets, etc. It is characterized by fast speed, but the effect on ambiguity segmentation is not g...
Examples
Embodiment Construction
[0037] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0038] Please refer to figure 1 , figure 1 It is a schematic flowchart of an embodiment of a method for establishing a word segmentation model in the present invention. Such as figure 1 As shown, the method includes:
[0039] Step S101: labeling each lexical entry and the part of speech of each lexical entry to the training corpus.
[0040] Step S102: Determine the part of speech of each entry under the corresponding part of speech.
[0041] Step S103: Use the marked training corpus to calculate the generation probability of each entry under the corresponding part of speech and the transition probability between each part of speech.
[0042] Step S104: Use the generation probability of each word entry under the corresponding part of speech to...