CN112926320B - Text key content intelligent extraction method and system based on subject term optimization - Google Patents
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Abstract
The utility model discloses a text key content intelligence extraction method level system based on subject term optimization, includes: acquiring a text to be recognized; performing chapter-level word segmentation on a text to be recognized, and acquiring chapter-level words and the weight of each chapter-level word; performing paragraph-level word segmentation on a text to be recognized, and acquiring paragraph-level words and part of speech of each paragraph-level word; matching the chapter-level words and the weights with the paragraph-level words and parts of speech, and outputting tuples comprising the words, the parts of speech and the weights; matching the tuple containing the words, the part of speech and the weight with a key phrase rule base to obtain a key phrase according with the rule; and acquiring text key content according to the key phrase. The method and the device realize accurate extraction of the key content of the text to be recognized.
Description
Technical Field
The invention relates to the technical field of text extraction, in particular to a text key content intelligent extraction method and system based on subject term optimization.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the continuous development of big data and artificial intelligence technology, massive, intelligent and diversified text information plays more and more important roles in work and life. When reading text information, especially text information of long space, it is time consuming, easily interfered by redundant information and easily losing key information. Therefore, a method for accurately extracting the key content from the text information is needed.
Disclosure of Invention
In order to solve the above problems, the present disclosure provides a text key content intelligent extraction method and system based on subject term optimization, so as to realize accurate extraction of text key content.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
in a first aspect, a text key content intelligent extraction method based on subject term optimization is provided, which includes:
acquiring a text to be recognized;
performing chapter-level word segmentation on a text to be recognized, and acquiring chapter-level words and the weight of each chapter-level word;
performing paragraph-level word segmentation on a text to be recognized to obtain paragraph-level words and part-of-speech of each paragraph-level word;
matching the chapter-level words and the weights with the paragraph-level words and parts of speech, and outputting tuples comprising the words, the parts of speech and the weights;
matching the tuple containing the words, the part of speech and the weight with a key phrase rule base to obtain a key phrase according with the rule;
and acquiring text key content according to the key phrase.
In a second aspect, an intelligent extraction system for text key content based on subject term optimization is provided, which includes:
the text acquisition module is used for acquiring a text to be identified;
the chapter-level word acquisition module is used for performing chapter-level word segmentation on the text to be recognized and acquiring chapter-level words and the weight of each chapter-level word;
the paragraph level word obtaining module is used for carrying out paragraph level word segmentation on the text to be recognized and obtaining paragraph level words and the part of speech of each paragraph level word;
the tuple acquisition module is used for matching the chapter-level words and the weights with the paragraph-level words and parts of speech and outputting tuples comprising the words, the parts of speech and the weights;
the key phrase acquisition module is used for matching tuples containing words, parts of speech and weights with a key phrase rule base to obtain key phrases which accord with rules;
and the text key content acquisition module is used for acquiring the text key content according to the key phrase.
In a third aspect, an electronic device is provided, which includes a memory, a processor, and computer instructions stored in the memory and executed on the processor, where the computer instructions, when executed by the processor, perform the steps of the text key content intelligent extraction method based on subject word optimization.
In a fourth aspect, a computer-readable storage medium is provided for storing computer instructions, which when executed by a processor, perform the steps of a text key content intelligent extraction method based on topic word optimization.
Compared with the prior art, this disclosed beneficial effect does:
1. according to the method, the text information is analyzed hierarchically, chapter-level words and paragraph-level words are divided for the text respectively, the obtained chapter-level words and paragraph-level words are matched, tuples comprising words, parts of speech and weight are obtained, key phrases are obtained according to the tuples, and further the key content of the text is obtained according to the key phrases, so that the key content of the text is extracted accurately.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
Fig. 1 is a flow chart of a method disclosed in embodiment 1 of the present disclosure.
The specific implementation mode is as follows:
the present disclosure is further illustrated by the following examples in conjunction with the accompanying drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only relational terms determined for convenience in describing structural relationships of the parts or elements of the present disclosure, and do not refer to any parts or elements of the present disclosure, and are not to be construed as limiting the present disclosure.
In the present disclosure, terms such as "fixedly connected", "connected", and the like are to be understood in a broad sense, and mean either a fixed connection or an integrally connected or detachable connection; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present disclosure can be determined on a case-by-case basis by persons skilled in the relevant art or technicians, and are not to be construed as limitations of the present disclosure.
Example 1
In this embodiment, an intelligent extraction method of text key content based on subject term optimization is disclosed, which includes:
acquiring a text to be identified;
performing chapter-level word segmentation on a text to be recognized, and acquiring chapter-level words and the weight of each chapter-level word;
performing paragraph-level word segmentation on a text to be recognized to obtain paragraph-level words and part-of-speech of each paragraph-level word;
matching the chapter-level words and the weights with the paragraph-level words and parts of speech, and outputting tuples comprising the words, the parts of speech and the weights;
matching the tuple containing the words, the part of speech and the weight with a key phrase rule base to obtain a key phrase according with the rule;
and acquiring the key content of the text according to the key phrase.
Furthermore, chapter-level word segmentation is carried out on the text to be recognized by adopting the ending word segmentation.
Furthermore, each paragraph of the text to be recognized is divided into sentences to obtain all sentences of each paragraph;
and segmenting each sentence to obtain paragraph-level words.
Furthermore, tuples containing words, parts of speech and weights are matched with a key phrase rule base according to the sentence forming sequence, tuples meeting key phrase rules are spliced according to the sequence, and key phrases are output.
Further, according to the word part of speech, outputting a key phrase rule, and defining the key phrase rule as a part of speech combination form.
Further, matching the key phrases with the text topic vocabulary to obtain the text key content.
Further, the specific process of obtaining the text key content is as follows:
matching the key phrases with subject words in the text subject word list to obtain text key content after the subject words are optimized;
performing null value analysis on the text key content after the subject term optimization to obtain the text key content corresponding to the null text;
and replacing the text key content corresponding to the empty text with the empty text in the text key content after the subject word optimization to obtain the final text key content.
The method for intelligently extracting key contents of a text based on subject term optimization disclosed in this embodiment is described in detail, and as shown in fig. 1, includes the following processes:
s1: and acquiring a text to be recognized.
In specific implementation, the html code is processed to obtain a text to be recognized, specifically:
s11: filtering < style >, < scripts > and < comments > in the html code corresponding to the text sequence to be recognized, and outputting an html structure code containing the text sequence to be recognized;
s12: creating and outputting a structure text sequence html iterator;
s13: and traversing each node of the html iterator, and outputting a structural sequence text with a complete paragraph level, wherein the structural sequence text is a text to be identified.
S2: and performing chapter-level word segmentation on the text to be recognized, and acquiring chapter-level words and the weight of each chapter-level word.
In the specific implementation:
s21: segmenting the text to be recognized by using the ending segmentation to obtain all words of the text, namely chapter-level words;
s22: converting all discourse-level words into a word-frequency matrix by adopting a CountVector () method;
s23: and calculating the TF-IDF weight of each chapter-level word by adopting a TffTransformer () method so as to obtain the chapter-level words and the weight corresponding to each chapter-level word.
S3: and performing paragraph-level word segmentation on the text to be recognized to obtain paragraph-level words and the part of speech of each paragraph-level word.
In the specific implementation:
s31: taking periods, semicolons and exclamation marks as clause rules, carrying out clause processing on each paragraph of the text to be recognized, and outputting all sentences of each paragraph;
s32: cut () method is adopted to segment the sentence obtained in S31, and all words of the sentence are output as paragraph level words and corresponding parts of speech of each paragraph level word.
S4: matching the chapter-level words and the weights with the paragraph-level words and parts of speech, and outputting tuples comprising the words, the parts of speech and the weights.
In specific implementation, the discourse-level words and discourse-level word weights output in S2 are matched with the paragraph-level words and corresponding parts of speech output in S3 one by one, and a tuple of < words, parts of speech, and weights > is output.
S5: and matching the tuple containing the words, the part of speech and the weight with a key phrase rule base to obtain the key phrase according with the rule.
In the specific implementation:
s51: constructing a key phrase rule base: manually inducing and summarizing word parts of speech, outputting key phrase rules, and defining the key phrase rules as a word part combination form as shown in a table 1;
TABLE 1 Key phrase rules
| Serial number | Key phrase rules | Serial number | Key phrase rules |
| 1 | (n,vn,n) | 4 | (n,n) |
| 2 | (n,a,vn) | 5 | (n,n,n) |
| 3 | (n,v,n) | 6 | (vn,vn,n) |
S52: and (3) matching the tuples of the words, the parts of speech and the weights output by the step (S4) with a key phrase rule base according to the sequence formed by the words of the sentences, splicing the tuples which accord with the key phrase rules according to the sequence, and outputting a key phrase v1.
S53: merging and filtering the key phrase v1 according to the key phrase processing rule shown in the table 2, and outputting the key phrase which accords with the rule;
TABLE 2 Key phrase handling rules
S6: and acquiring the key content of the text according to the key phrase.
In the specific implementation:
s61: constructing a text topic word list according to the article topic classification: and (5) manually summarizing and summarizing according to the article topics, and outputting a text topic word list as shown in a table 3.
TABLE 3 topic word Table
S62: and matching and filtering the key phrases output by the step S5 and the subject words in the subject word list, and outputting the text key content after the optimization of the subject words.
S63: and performing null value analysis on the text key content after the subject term optimization output in the step S62, and analyzing the identified null text to obtain the text key content corresponding to the null text.
And if the key content corresponding to the text is empty, adopting semantic role analysis based on LTP, and outputting the implementer, the verb and the subject as the key content of the text.
S64: and replacing the empty text in the text key content after the subject term optimization output by the S62 with the text key content corresponding to the empty text output by the S63 to obtain the final text key content.
In the embodiment, the text information is subjected to hierarchical analysis, a hierarchical key content intelligent extraction method for sections, paragraphs, sentences and article topics is respectively constructed, and a text key content intelligent extraction method based on subject term optimization is provided, so that the text key content is extracted quickly, efficiently and accurately, the working efficiency is greatly improved, and the labor cost is saved.
Example 2
In this embodiment, an intelligent extraction system for text key content based on subject term optimization is disclosed, which includes:
the text acquisition module is used for acquiring a text to be identified;
the chapter-level word acquisition module is used for performing chapter-level word segmentation on the text to be recognized and acquiring chapter-level words and the weight of each chapter-level word;
the paragraph level word obtaining module is used for carrying out paragraph level word segmentation on the text to be recognized and obtaining paragraph level words and the part of speech of each paragraph level word;
the tuple obtaining module is used for matching the chapter-level words and the weights with the paragraph-level words and parts of speech and outputting tuples comprising the words, the parts of speech and the weights;
the key phrase acquisition module is used for matching tuples containing words, parts of speech and weights with a key phrase rule base to obtain key phrases according with rules;
and the text key content acquisition module is used for acquiring the text key content according to the key phrase.
Example 3
In this embodiment, an electronic device is disclosed, which comprises a memory, a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps of the text key content intelligent extraction method based on subject word optimization disclosed in embodiment 1 are completed.
Example 4
In this embodiment, a computer-readable storage medium is disclosed for storing computer instructions, and when the computer instructions are executed by a processor, the computer instructions perform the steps of the text key content intelligent extraction method based on subject term optimization disclosed in embodiment 1.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (8)
1. A text key content intelligent extraction method based on subject term optimization is characterized by comprising the following steps:
acquiring a text to be identified;
performing chapter-level word segmentation on a text to be recognized, and acquiring chapter-level words and the weight of each chapter-level word;
performing paragraph-level word segmentation on a text to be recognized, and acquiring paragraph-level words and part of speech of each paragraph-level word;
matching the chapter-level words and the weights with the paragraph-level words and parts of speech, and outputting tuples comprising the words, the parts of speech and the weights;
matching the tuple containing the words, the part of speech and the weight with a key phrase rule base to obtain a key phrase according with the rule;
matching the key phrases with the text subject word list according to the key phrases to obtain the key content of the text;
the specific process for acquiring the text key content comprises the following steps:
matching the key phrases with subject words in the text subject word list to obtain text key content after the subject words are optimized;
performing null value analysis on the text key content after the subject term optimization to obtain text key content corresponding to a null text;
and replacing the empty text in the text key content after the subject word optimization by the text key content corresponding to the empty text to obtain the final text key content.
2. The method for intelligently extracting key contents of a text based on topic word optimization as claimed in claim 1, wherein chapter-level segmentation is performed on the text to be recognized by using crust segmentation.
3. The method according to claim 1, wherein the process of obtaining paragraph-level words comprises:
each paragraph of the text to be recognized is divided into sentences to obtain all sentences of each paragraph;
and segmenting each sentence to obtain paragraph-level words.
4. The intelligent extraction method of key contents of texts based on subject word optimization as claimed in claim 1, wherein the tuples containing words, parts of speech and weights are matched with the key phrase rule base according to the sentence composition sequence, the tuples meeting the key phrase rules are spliced in sequence, and the key phrases are output.
5. The method as claimed in claim 4, wherein the key phrase rules are outputted according to the part of speech of the words.
6. A text key content intelligent extraction system based on subject term optimization is characterized by comprising:
the text acquisition module is used for acquiring a text to be identified;
the chapter-level word acquisition module is used for performing chapter-level word segmentation on the text to be recognized and acquiring chapter-level words and the weight of each chapter-level word;
the paragraph level word obtaining module is used for carrying out paragraph level word segmentation on the text to be recognized and obtaining paragraph level words and the part of speech of each paragraph level word;
the tuple obtaining module is used for matching the chapter-level words and the weights with the paragraph-level words and parts of speech and outputting tuples comprising the words, the parts of speech and the weights;
the key phrase acquisition module is used for matching tuples containing words, parts of speech and weights with a key phrase rule base to obtain key phrases which accord with rules;
the text key content acquisition module is used for matching the key phrases with the text subject word list according to the key phrases so as to acquire text key content;
the specific process for acquiring the text key content comprises the following steps:
matching the key phrases with subject words in the text subject word list to obtain text key content after the subject words are optimized;
performing null value analysis on the text key content after the subject term optimization to obtain the text key content corresponding to the null text;
and replacing the empty text in the text key content after the subject word optimization by the text key content corresponding to the empty text to obtain the final text key content.
7. An electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the intelligent extraction method for text key content based on subject word optimization according to any one of claims 1 to 5.
8. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the intelligent extraction method for text key content based on subject term optimization according to any one of claims 1 to 5.
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| CN110413998A (en) * | 2019-07-16 | 2019-11-05 | 深圳供电局有限公司 | Self-adaptive Chinese word segmentation method, system and medium for power industry |
| CN111274806A (en) * | 2020-01-20 | 2020-06-12 | 医惠科技有限公司 | Method and device for recognizing word segmentation and part of speech and method and device for analyzing electronic medical record |
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| CA2774278C (en) * | 2009-09-25 | 2018-10-30 | Shady Shehata | Methods and systems for extracting keyphrases from natural text for search engine indexing |
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| CN110298028A (en) * | 2019-05-21 | 2019-10-01 | 浙江省北大信息技术高等研究院 | A kind of the critical sentence extracting method and device of text fragment |
| CN110413998A (en) * | 2019-07-16 | 2019-11-05 | 深圳供电局有限公司 | Self-adaptive Chinese word segmentation method, system and medium for power industry |
| CN111274806A (en) * | 2020-01-20 | 2020-06-12 | 医惠科技有限公司 | Method and device for recognizing word segmentation and part of speech and method and device for analyzing electronic medical record |
| CN111767393A (en) * | 2020-06-22 | 2020-10-13 | 中国建设银行股份有限公司 | Text core content extraction method and device |
| CN112182141A (en) * | 2020-09-25 | 2021-01-05 | 中国建设银行股份有限公司 | Key information extraction method, device, equipment and readable storage medium |
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