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Establishment method and system for bilingual semantic relation classification model

A semantic relationship and classification model technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve time-consuming and labor-intensive problems, achieve the effect of reducing demand and improving establishment efficiency

Inactive Publication Date: 2014-02-05
SUZHOU UNIV
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Problems solved by technology

For these two methods, the quantity and quality of the labeled corpus usually determine the performance of relation extraction, and the labeling of the corpus needs to be done manually, which is laborious and time-consuming

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  • Establishment method and system for bilingual semantic relation classification model
  • Establishment method and system for bilingual semantic relation classification model

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Embodiment Construction

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0050] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0051] figure 1 It is a flow chart of Embodiment 1 of the establishment method of the bilingual semantic relationship classification model of the present invention. Such as figure 1 As shown, the method may incl...

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Abstract

The invention discloses an establishment method and system for a bilingual semantic relation classification model. The method comprises the followings: semantic relation classification is performed on the first un-annotated corpus of an un-annotated semantic relation, which corresponds to first language, through a first language classifier, so as to obtain a semantic relation classification result; the first un-annotated corpus is translated into a second un-annotated corpus corresponding to a second language; the corresponding relation of the first un-annotated corpus and the semantic relation classification result is stored into a first training set of the first language classifier; the corresponding relation of the second un-annotated corpus and the semantic relation classification result is stored into a second training set of the second language classifier. After the adoption of the method or the system provided by the invention, the demands for large-scale training corpus can be reduced, and the establishing efficiency for the bilingual semantic relation classification model can be improved.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to a method and system for establishing a bilingual semantic relationship classification model. Background technique [0002] Semantic relationship extraction between named entities (referred to as entity relationship extraction, or relationship extraction) is an important research content in the field of information extraction and natural language processing. Its task is to extract the semantic relationship between two named entities from natural language text. For example, the semantic relationship between the two entities "Clinton" (PER) and "Pyongyang" (GPE) in the phrase "U.S. President Clinton's trip to Pyongyang" is a physical location relationship (PHYS.located). [0003] The main method of relation extraction is to control the machine to learn semantic relations based on labeled corpus and unlabeled corpus, and then establish a classification model for ...

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Application Information

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IPC IPC(8): G06F17/28
Inventor 钱龙华胡亚楠周国栋
Owner SUZHOU UNIV
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