Automatic extraction method for case semantic elements
An automatic extraction and element technology, applied in the field of natural language processing and machine learning, can solve problems such as vocabulary gap, algorithm performance impact, characterize word and word similarity, etc., so as to improve extraction efficiency, improve case handling efficiency, and reduce manual extraction cost effect
Inactive Publication Date: 2018-04-10
武汉烽火普天信息技术有限公司
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Problems solved by technology
[0007] Aiming at the deficiencies of the prior art, the present invention provides an automatic extraction method for the semantic elements of a case, which solves the problem of dimensionality disaster, especially when used as the input of a deep learning algorithm, the performance of the algorithm is seriously affected; there is a "lexical gap" Phenomenon, that is to say, the problem of not being able to describe the similarity between words well
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[0019] 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.
[0020] An embodiment of the present invention provides a method for automatically extracting semantic elements of a case, such as figure 1 shown, including the following steps:
[0021] S1. First, according to the part of speech that may appear in the elements, use the pre-trained sequence tagging model to tag the part of speech of the brief case, and identify the nouns, verbs and gerunds in it; if the object involved in the case is a noun, the crime tool is a...
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The invention discloses an automatic extraction method for case semantic elements. The automatic extraction method comprises the following steps: (1) carrying out part-of-speech tagging on a brief case text by virtue of a sequence tagging module according to a pre-trained Chinese sequence tagging model, so as to recognize nouns, verbs and gerunds in the brief case text; (2) enriching a seed lexicon through word meaning similarity algorithm by virtue of a word meaning similarity module; (3) carrying out manual correction for several times by virtue of an adjusting-optimizing module, so as to further adjust and optimize an element extraction result; and (4) creating a classifier library through the elements according to the category by virtue of a classifying lexicon module. According to theautomatic extraction method, the case semantic elements are automatically extracted from a detailed, complicated and ruleless brief case, the semantic element extraction efficiency of the case is greatly improved, and the manual extraction cost is lowered; and meanwhile, a system can be applied to semantic comprehension analysis of other unstructured text information of public security industry,and an effective method is provided for correlation analysis of the cases, so that the case treating efficiency is improved.
Description
technical field [0001] The invention relates to the technical fields of natural language processing and machine learning, in particular to an automatic extraction method of case semantic elements. Background technique [0002] With the rapid development of natural language processing and machine learning, these technologies have been widely used in search engines, intelligent translation, machine question answering, user recommendation, public opinion monitoring and other related industries. Public security organs have accumulated a large amount of data information in the long-term informatization process, but the application of data at this stage is still limited to simple query of structured data, and has not yet carried out in-depth application of unstructured or semi-structured data. At the same time, there are still many problems in these unstructured or semi-structured data: the information format is not uniform, and the information description is diversified. Based o...
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Login to View More IPC IPC(8): G06F17/27
CPCG06F16/3344G06F40/289G06F40/30
Inventor 金勇邱爽李力陈云浩朱阳光
Owner 武汉烽火普天信息技术有限公司