CN101561818B - Method for word segmentation processing and method for full-text retrieval - Google Patents
Method for word segmentation processing and method for full-text retrieval Download PDFInfo
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- CN101561818B CN101561818B CN200910083775.9A CN200910083775A CN101561818B CN 101561818 B CN101561818 B CN 101561818B CN 200910083775 A CN200910083775 A CN 200910083775A CN 101561818 B CN101561818 B CN 101561818B
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Abstract
The invention provides a method for word segmentation processing and application of the same in the full-text retrieval of a database. The method comprises the following steps: creating a new word segmentation system based on the characteristic items of the database and adding the characteristic items of the database into the new word segmentation system; and using query words submitted by a user and the characteristic items of the database as a word list to perform the word segmentation processing to generate a word segmentation result set. According to the method provided by the invention, fields in the database are selected as the characteristic items for performing word segmentation and the association between the characteristic items of the database and the texts in the data base are used, so the word segmentation accuracy of unary, binary, and word list-presetting conventional word segmentation methods and other conventional word segmentation methods is improved effectively.
Description
Technical field
The present invention relates to full-text search, relate more specifically to segmenting method in full-text search and the application in full-text search thereof.
Background technology
Word segmentation result after existing text retrieval system based on database utilizes one-gram word, binary participle more or presets the vocabulary participle is carried out the database full-text search.
Such as, query word " database software ", handling the back through one-gram word is " number, certificate, storehouse, soft, part ", system is combined into the full-text search of line data storehouse with this word segmentation result as query word afterwards.But such result for retrieval is very inaccurate.Only, just make that the text that comprises " Coca-Cola has been issued a new soft drink " also can appear in the result for retrieval because " soft " this one-gram word result occurs therein.
Use binary word segmentation result " data, according to storehouse, soft, the software in storehouse " as query word, above-mentioned text can not be retrieved out, improved the accuracy of result for retrieval to a certain extent, but picture " this software has adopted the asynchronous data processing mode " is this to be comprised the binary word segmentation result but still can be retrieved with the irrelevant text of inquiry, can not avoid the same problem that occurs in the one-gram word fully.
The accuracy that the vocabulary participle can improve participle is more accurately preset in use, but its prerequisite is that to preset the vocabulary of vocabulary enough big, comprise " database, software " if preset in the vocabulary, thereby query word can be divided into " database, software " accurately be improved result for retrieval." if software, data " in presetting vocabulary and " database " not therein, word segmentation result will be " data, storehouse, software ", the false retrieval result that this can not be avoided monobasic or binary participle to be occurred equally.And because preset the vocabulary relative fixed of vocabulary, and new vocabulary emerges in an endless stream, and can both exert an influence so the accuracy that table carries out participle is preset in use.
Nowadays, in enterprise database, the many forms with metadata of structural data are stored in the field of database table, and how in full this form of semi-structured or unstructured data is stored.But structuring, semi-structured and unstructured data mostly are the field related content of enterprise, exist the association on the certain degree each other.The effect of participle is with non-structured query word structuring in the full-text search, retrieves non-structured text then.Therefore, suitably choose structured features item in the enterprise database, help to judge more exactly query word and be retrieved the degree of correlation of text, thereby reach the effect of optimizing full-text search as the foundation of participle.
Therefore, in order to solve indeterminable technical matters in the above-mentioned existing segmenting method,, proposed a kind of segmenting method and used the text searching method of this segmenting method at enterprise database.
Summary of the invention
In order one of to solve the problems of the technologies described above at least, the invention provides a kind of participle processing method, it is characterized in that, comprising: create new Words partition system, and described database feature item is added in the described new Words partition system based on the database feature item; And the query word that the user submits to carried out participle with the described database feature item in the described new Words partition system as vocabulary, to generate the word segmentation result collection.
In technique scheme, can also comprise:, the described word segmentation result collection that is generated is divided into the first participle subclass and do not comprise the second word segmentation result subclass of described database feature item as a result that comprises described database feature item based on described database feature item; Use other Words partition systems that are different from described new Words partition system to carry out word segmentation processing to the described second word segmentation result subclass to generate the 3rd word segmentation result subclass; And with the described first participle subclass and described the 3rd word segmentation result subclass merge and obtain new word segmentation result collection as a result.
Wherein, other Words partition systems comprise: one-gram word system, binary Words partition system or preset the vocabulary Words partition system.The database feature item comprises: table in the database and field.
According to a further aspect in the invention, the present invention also provides a kind of text searching method, is used for carrying out full-text search at enterprise database, it is characterized in that, comprise: create new Words partition system, and described database feature item is added in the described new Words partition system based on the database feature item; The query word that the user is submitted to carries out participle with the described database feature item in the described new Words partition system as vocabulary, to generate the word segmentation result collection; Based on described database feature item, the described word segmentation result collection that is generated is divided into the first participle subclass and do not comprise the second word segmentation result subclass of described database feature item as a result that comprises described database feature item; Use other Words partition systems that are different from described new Words partition system to carry out word segmentation processing to the described second word segmentation result subclass to generate the 3rd word segmentation result subclass; Subclass and described the 3rd word segmentation result subclass merge and obtain new word segmentation result collection as a result with the described first participle; And carry out full-text search as query word set with described new word segmentation result collection, obtain the result for retrieval text set.
In technique scheme, can also comprise: described result for retrieval text set and described new word segmentation result collection are carried out relatedness computation; And described result for retrieval text set is sorted, and return as Query Result according to the described degree of correlation that calculates.
In technique scheme, before returning described Query Result, also comprise: set degree of correlation threshold value for the described degree of correlation, get rid of the low excessively null result of the degree of correlation.
In technique scheme, the degree of correlation is calculated by following formula:
Wherein, i is the number of the branch lexical item in the described new word segmentation result, and query
iBe i branch lexical item, and Text is the retrieval text.
In technique scheme, other Words partition systems comprise: one-gram word system, binary Words partition system or preset the vocabulary Words partition system.The database feature item comprises: table in the database and field.
According to participle processing method of the present invention and text searching method, the participle processing method of a kind of binding data storehouse characteristic item has been proposed, and the search method in the enterprise database text retrieval system.It helps to judge more exactly query word and is retrieved the degree of correlation of text and the accuracy rate of having improved the retrieval rate and the relevance ranking of full-text search by suitably choosing structured features item in the enterprise database as the foundation of participle.
Description of drawings
The present invention is further detailed explanation below in conjunction with the drawings and specific embodiments.
Fig. 1 is the process flow diagram according to participle processing method of the present invention;
Fig. 2 is the process flow diagram of participle processing method according to an embodiment of the invention;
Fig. 3 is the process flow diagram according to text searching method of the present invention; And
Fig. 4 is the process flow diagram of text searching method according to an embodiment of the invention.
Embodiment
Below will be in detail with reference to the preferred embodiments of the present invention, the example of preferred embodiment is described with reference to the accompanying drawings.
Fig. 1 is the process flow diagram according to participle processing method of the present invention.As shown in Figure 1, segmenting method according to the present invention may further comprise the steps: step S102, create the new Words partition system based on the database feature item, and the database feature item added in the new Words partition system; And step S104, the query word that the user is submitted to carries out participle with the database feature item in this new Words partition system as vocabulary, to generate the word segmentation result collection.In participle processing method according to the present invention, other Words partition systems can be one-gram word system, binary Words partition system or preset the vocabulary Words partition system.And the database feature item can be table or field in the database.
Fig. 2 is a word segmentation processing according to an embodiment of the invention system.In shown in Figure 2 word segmentation processing according to an embodiment of the invention system,, also comprise and use other Words partition systems to carry out participle using after Words partition system based on the database feature item carries out participle.As shown in Figure 2, this word segmentation processing system comprises: S202, create the new Words partition system based on the database feature item, and the database feature item added in the new Words partition system; S204, the query word that the user is submitted to carries out participle with the database feature item in this new Words partition system as vocabulary, to generate the word segmentation result collection; S206 based on the database feature item, is divided into the word segmentation result collection that is generated the first participle subclass and do not comprise the second word segmentation result subclass of database feature item as a result that comprises the database feature item; S208 uses other Words partition systems that are different from new Words partition system to carry out word segmentation processing to generate the 3rd word segmentation result subclass to the second word segmentation result subclass; And S210, subclass and described the 3rd word segmentation result subclass merge and obtain new word segmentation result collection as a result with the first participle.
Above-mentioned according to participle processing method of the present invention in, other Words partition systems can be but be not limited to, one-gram word system, binary Words partition system or preset the vocabulary Words partition system.And the database feature item can be table or field in the database.
In participle processing method of the present invention, solved the problem of the inaccurate result for retrieval that leads to errors of participle in the prior art, it carries out participle and can carry out participle more exactly according to the database feature item.
Fig. 3 is the process flow diagram according to text searching method of the present invention.As shown in Figure 3, text searching method according to the present invention comprises: S302, create the new Words partition system based on the database feature item, and described database feature item added in the new Words partition system; S304, the query word that the user is submitted to carries out participle with the database feature item in this new Words partition system as vocabulary, to generate the word segmentation result collection; S306 based on the database feature item, is divided into the word segmentation result collection that is generated the first participle subclass and do not comprise the second word segmentation result subclass of database feature item as a result that comprises the database feature item; S308 uses other Words partition systems that are different from new Words partition system to carry out word segmentation processing to generate the 3rd word segmentation result subclass to the second word segmentation result subclass; S310, subclass and the 3rd word segmentation result subclass merge and obtain new word segmentation result collection as a result with the first participle; And S312, carry out full-text search with new word segmentation result collection as query word set, obtain the result for retrieval text set.
In technique scheme, can also comprise: result for retrieval text set and new word segmentation result collection are carried out relatedness computation; And the result for retrieval text set is sorted, and return as Query Result according to the degree of correlation that calculates.
In technique scheme, before returning Query Result, also comprise: set degree of correlation threshold value for the degree of correlation, get rid of the low excessively null result of the degree of correlation.
In technique scheme, the degree of correlation is calculated by following formula:
Wherein, i is the number of the branch lexical item in the new word segmentation result, and query
iBe i branch lexical item, and Text is the retrieval text.
In technique scheme, it should be appreciated by those skilled in the art that other Words partition systems can include but not limited to: one-gram word system, binary Words partition system or preset the vocabulary Words partition system.The database feature item can comprise: table in the database and field.
Fig. 4 is the process flow diagram of text searching method according to an embodiment of the invention.As shown in Figure 4, at first, step S402, table and field are as characteristic item in the selected data storehouse; Secondly, step S404 joins Words partition system based on the database feature item with characteristic item, uses the query word of the user being submitted to based on the Words partition system of database feature item to carry out participle to generate word segmentation result collection result; Once more, step S406 filters out the set result that comprises entry in the database feature item in result
1, and step S408, the set result of entry in the non-database feature item
2Then, step S410 is to result
2Middle entry uses other Words partition systems to carry out participle, and step S412 generates word segmentation result set result
2'; Then, step S414 is with result
1With result
2' merge into result '; Then, step S416 carries out full-text search with result ' as query word set, and step S418 obtains result for retrieval text set text; Then, step S420 carries out relatedness computation to text and result ' in the text correlation computing system; At last, step S422 sorts to text to low from height by the degree of correlation, and returns as Query Result.
In this embodiment, the degree of correlation is calculated by following formula:
Wherein, i is the number of the branch lexical item in the new word segmentation result, and query
iBe i branch lexical item, and Text is the retrieval text.
Listed below according to existing segmenting method, and the comparative example that adopts segmenting method provided by the invention to carry out participle and carry out full-text search.In this comparative example, suppose:
Query word: database software
Preset vocabulary: data, software
The database feature item: database, middleware, browser, terminating machine ...
Text:
Text
1: Coca-Cola has been issued a new soft drink.
Text
2: the dynamic link library of this software has adopted the asynchronous data processing mode.
Text
3: the operation of most of server softwares all need be by means of data database storing.
By top hypothesis, listed the word segmentation result that adopts different segmenting methods to obtain below:
(1) adopt one-gram word:
Query word word segmentation result: number, certificate, storehouse, soft, part
Result for retrieval is got union: Text
1, Text
2, Text
3
(2) adopt the binary participle:
Query word word segmentation result: data, according to storehouse, soft, the software in storehouse
Result for retrieval is got union: Text
2, Text
3
(3) the vocabulary participle is preset in employing:
Query word word segmentation result: data, storehouse, software
Result for retrieval is got union: Text
2, Text
3
(4) adopt data-base recording item participle+one-gram word:
Word segmentation result: database, soft, part
Result for retrieval is got union: Text
1, Text
2, Text
3
(5) adopt data-base recording item participle+binary participle:
Word segmentation result: database, software
Result for retrieval is got union: Text
3
(6) adopt data-base recording item participle+preset vocabulary participle:
Word segmentation result: database, software
Result for retrieval is got union: Text
3
The degree of correlation that calculates according to formula 1 and formula 2 is as follows:
(1) adopt one-gram word:
Word segmentation result: number, certificate, storehouse, soft, part
Result for retrieval is got union: R (Query, Text
1)=0.2, R (Query, Text
2)=1, R (Query, Text
3)=1;
(2) adopt the binary participle:
Word segmentation result: data, according to storehouse, soft, the software in storehouse
Result for retrieval is got union: R (Query, Text
2)=0.5, R (Query, Text
3)=0.75;
(3) the vocabulary participle is preset in employing:
Word segmentation result: data, storehouse, software
Result for retrieval: R (Query, Text
2)=1, R (Query, Text
3)=1;
(4) adopt data-base recording item participle+one-gram word:
Word segmentation result: database, soft, part
Result for retrieval: R (Query, Text
1)=0.3333, R (Query, Text
2)=0.6667, R (Query, Text
3)=1;
(5) adopt data-base recording item participle+binary participle:
Word segmentation result: database, software
Result for retrieval: R (Query, Text
3)=1;
(6) adopt data-base recording item participle+preset vocabulary participle:
Word segmentation result: database, software
Result for retrieval: R (Query, Text
3)=1.
To description according to an embodiment of the invention, the method that the present invention proposes is based on the full-text search of database by above-mentioned, and range of search is confined to the text in the database.Field is carried out participle as characteristic item in the selected data storehouse according to the proposed method, utilized the incidence relation of text in database feature item and the database, the participle accuracy of improved monobasic, binary effectively, presetting traditional segmenting methods such as vocabulary.Simultaneously, a kind of relatedness computation method of new binding data storehouse characteristic item word segmentation result has also been proposed, the result of calculation of this method provides the foundation of ordering for the output of result for retrieval, thereby will export to the user with the highest text of the query word degree of correlation forwardly, and can set degree of correlation threshold value, get rid of the low excessively null result of the degree of correlation.
Although described embodiment with reference to a plurality of exemplary embodiment, it will be appreciated by those skilled in the art that and can design a plurality of other modifications and embodiment, fall into the spirit and the concept of this instructions.More specifically, the ingredient of the layout of this combination in the scope of this instructions, accompanying drawing and appending claims and/or the variations and modifications in the layout are possible.Except ingredient and/or the variation and modification in arranging, alternative use is obvious to those skilled in the art.
Claims (7)
1. a participle processing method is characterized in that, comprising:
Establishment is based on the new Words partition system of database feature item, and described database feature item is added in the described new Words partition system; And
The query word that the user is submitted to carries out word segmentation processing with the described database feature item in the described new Words partition system as vocabulary, to generate the word segmentation result collection;
Based on described database feature item, the described word segmentation result collection that is generated is divided into the first participle subclass and do not comprise the second word segmentation result subclass of described database feature item as a result that comprises described database feature item;
Use other Words partition systems that are different from described new Words partition system to carry out word segmentation processing to the described second word segmentation result subclass to generate the 3rd word segmentation result subclass; And
With the described first participle as a result subclass and described the 3rd word segmentation result subclass merge and obtain new word segmentation result collection,
Wherein, described other Words partition systems comprise: one-gram word system, binary Words partition system or preset the vocabulary Words partition system.
2. participle processing method according to claim 1 is characterized in that, described database feature item comprises: table in the database and field.
3. a text searching method is used for carrying out full-text search at enterprise database, it is characterized in that, comprising:
Establishment is based on the new Words partition system of database feature item, and described database feature item is added in the described new Words partition system;
The query word that the user is submitted to carries out word segmentation processing with the described database feature item in the described new Words partition system as vocabulary, to generate the word segmentation result collection;
Based on described database feature item, the described word segmentation result collection that is generated is divided into the first participle subclass and do not comprise the second word segmentation result subclass of described database feature item as a result that comprises described database feature item;
Use other Words partition systems that are different from described new Words partition system to carry out word segmentation processing to the described second word segmentation result subclass to generate the 3rd word segmentation result subclass;
Subclass and described the 3rd word segmentation result subclass merge and obtain new word segmentation result collection as a result with the described first participle; And
Carry out full-text search with described new word segmentation result collection as query word set, obtain the result for retrieval text set,
Described other Words partition systems comprise: one-gram word system, binary Words partition system or preset the vocabulary Words partition system.
4. text searching method according to claim 3 is characterized in that, also comprises:
Described result for retrieval text set and described new word segmentation result collection are carried out relatedness computation; And
According to the described degree of correlation that calculates described result for retrieval text set is sorted, and return as Query Result.
5. text searching method according to claim 4 is characterized in that, before returning described Query Result, also comprises: set degree of correlation threshold value for the described degree of correlation, get rid of the low excessively null result of the degree of correlation.
6. text searching method according to claim 5 is characterized in that, carries out relatedness computation according to following formula:
Wherein, i is the number of the branch lexical item in the described new word segmentation result, and
Query
iBe i branch lexical item, and Text is the retrieval text.
7. according to each described text searching method in the claim 3 to 6, it is characterized in that described database feature item comprises: table in the database and field.
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| CN101968801A (en) * | 2010-09-21 | 2011-02-09 | 上海大学 | Method for extracting key words of single text |
| CN102890690B (en) * | 2011-07-22 | 2017-04-12 | 中兴通讯股份有限公司 | Target information search method and device |
| CN102982099B (en) * | 2012-11-05 | 2015-11-11 | 西安邮电大学 | A kind of personalized Parallel Word Segmentation disposal system and disposal route thereof |
| CN104765833B (en) * | 2015-04-13 | 2018-06-19 | 天脉聚源(北京)传媒科技有限公司 | A kind of generation method and device of word association table |
| CN105740231A (en) * | 2016-01-28 | 2016-07-06 | 浪潮软件股份有限公司 | Data content associating method and apparatus |
| CN111063447B (en) * | 2019-12-17 | 2024-05-03 | 北京懿医云科技有限公司 | Query and text processing method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101067809A (en) * | 2007-06-22 | 2007-11-07 | 蒋贤春 | Independent word segmentation |
| CN101079024A (en) * | 2006-06-19 | 2007-11-28 | 腾讯科技（深圳）有限公司 | Special word list dynamic generation system and method |
| CN101246492A (en) * | 2008-02-26 | 2008-08-20 | 华中科技大学 | Full text retrieval system based on natural language |
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| CN101079024A (en) * | 2006-06-19 | 2007-11-28 | 腾讯科技（深圳）有限公司 | Special word list dynamic generation system and method |
| CN101067809A (en) * | 2007-06-22 | 2007-11-07 | 蒋贤春 | Independent word segmentation |
| CN101246492A (en) * | 2008-02-26 | 2008-08-20 | 华中科技大学 | Full text retrieval system based on natural language |
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