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Entity instance leading method based on machine learning

A technology of ontology instance and learning method, which is applied in the field of natural language processing and ontology learning, can solve problems such as weak flexibility and too much manual participation, and achieve the effect of avoiding human research and improving performance

Inactive Publication Date: 2012-09-12
TIANJIN UNIV
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  • Application Information

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Problems solved by technology

At the same time, there are also deficiencies such as lack of flexibility and the need for too much manual participation.

Method used

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  • Entity instance leading method based on machine learning
  • Entity instance leading method based on machine learning
  • Entity instance leading method based on machine learning

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Embodiment Construction

[0018] The present invention introduces a machine learning method in the learning process. There are often many types and levels of concepts in ontology models, and machine learning methods can handle nuanced and fuzzy concepts, thereby effectively extracting ontology instances and attributes from text.

[0019] Maximum entropy is a commonly used model in machine learning. The main idea of ​​the maximum entropy model is to select the distribution that maximizes the entropy under the condition of satisfying the constraints. Classifiers based on this model are widely used in natural language processing, such as named entity recognition and part-of-speech tagging. The principle of using the maximum entropy model for entity classification is as follows: the context information of each entity is expressed as (x 1 , x 2 ,...,x m ), the category to which the entity belongs is expressed as (y 1 ,y 2 ,...,y p ). Then p(y x) represents the probability that the entity is classifi...

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Abstract

The invention belongs to the technical field of natural language treatment and entity leaning and relates to an entity instance leading method based on the machine learning. The method comprises the following steps of: carrying out linguistic data marking after document pretreatment, selecting various feathers including word feathers, word class feathers and combination feathers of words and word class feathers, and converting the linguistic data and texts to be identified into feature vector modes; carrying out the maximum entropy model training, and obtaining the maximum entropy classifier through the marked linguistic data maximum entropy model parameters; and carrying out instance extraction by the maximum entropy classifier. The method has the advantage that the entity instance can be fast and effectively leaned from a large number of texts.

Description

Technical field [0001] The invention relates to the technical fields of natural language processing and ontology learning. Mainly according to the characteristics of the ontology model, the method and experience of processing text based on machine learning in natural language processing are absorbed to learn ontology examples. Background technique [0002] At present, most of the information on the traditional network is unstructured, lacks organization, and there are a lot of useless and redundant information. The explosive growth of Internet information has brought more difficulties in processing information and acquiring knowledge. Ontology is a clear formal specification of shared concept model, and it plays a role in enabling good communication and understanding between service layers in Semantic Web. Ontology provides a knowledge base and a rule base for the establishment of the Semantic Web, based on which semantic search and intelligent work can be performed. At p...

Claims

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Application Information

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IPC IPC(8): G06F17/21G06F17/30
Inventor 张萌王文俊
Owner TIANJIN UNIV
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