Text matching method, device, equipment and storage medium

Abstract

The present application discloses a text matching method, apparatus, device and storage medium, including: obtaining input text and resource data to be matched; performing word segmentation on the input text and resource data to be matched based on words in a system vocabulary to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched; calculating the similarity between the first keyword phrase and the second keyword phrase, and determining the resource data corresponding to the input text from the resource data to be matched based on the similarity. The present application performs a word segmentation operation with a finer matching granularity on the input text and the resource data to be matched based on the system vocabulary to obtain the corresponding first keyword phrase and second keyword phrase, making the word segmentation result more accurate, and then determines the resource data in the resource data to be matched that matches the input text by calculating the similarity between the first keyword phrase and the second keyword phrase, thereby improving the text matching accuracy in lightweight scenarios.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a text matching method, apparatus, device and storage medium. Background Art

[0002] Traditional input matching methods rely on inclusive fuzzy matching based on user-entered keywords. This results in coarse-grained data matching and a relatively closed vocabulary. This often leads to inaccurate matching results and failure to obtain ideal matching resource data. Furthermore, existing ElasticSearch technology relies on building an ES server for search matching. However, this requirement makes it suitable for specialized search scenarios, such as high-volume search engines. However, for scenarios requiring a small, lightweight search tool, the cost of building an ES server is high, and text matching resource utilization is low in lightweight scenarios.

[0003] Therefore, how to improve the text matching accuracy in lightweight scenarios is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a text matching method, apparatus, device and storage medium to improve the text matching accuracy in lightweight scenarios. The specific solution is as follows:

[0005] A first aspect of the present application provides a text matching method, comprising:

[0006] Get the input text and resource data to be matched;

[0007] Segment the input text and the resource data to be matched based on the words in the system vocabulary to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched;

[0008] The similarity between the first keyword phrase and the second keyword phrase is calculated, and resource data corresponding to the input text is determined from the resource data to be matched according to the similarity.

[0009] Optionally, the input text and the resource data to be matched are segmented based on the words in the system vocabulary, including:

[0010] The words in the system vocabulary are stored by constructing a dictionary tree to obtain a target dictionary tree corresponding to the system vocabulary;

[0011] The target dictionary tree is traversed to match the phrases in the input text and the resource data to be matched respectively, so as to segment the input text and the resource data to be matched using the dictionary tree algorithm.

[0012] Optionally, calculating the similarity between the first keyword phrase and the second keyword phrase includes:

[0013] The similarity between the first keyword phrase and the second keyword phrase is calculated using a cosine similarity algorithm.

[0014] Optionally, the text matching method also includes:

[0015] Create a dynamic link library containing the dictionary tree algorithm and cosine similarity algorithm;

[0016] The input text and the resource data to be matched are segmented or the similarity between the first keyword phrase and the second keyword phrase is calculated by calling the corresponding algorithm from the dynamic link library.

[0017] Optionally, determining resource data corresponding to the input text from the resource data to be matched based on similarity includes:

[0018] It is determined whether the similarity is greater than a preset threshold. If so, the resource data corresponding to the second keyword phrase with a similarity greater than the preset threshold is determined as the resource data corresponding to the input text.

[0019] Optionally, after determining whether the similarity is greater than a preset threshold, the following steps may be further performed:

[0020] If the similarities are all less than a preset threshold, the first keyword phrase is matched with words in the associated vocabulary to obtain an associated phrase associated with the first keyword phrase; wherein the associated vocabulary includes a plurality of word pairs that have associated relationships in the historical matching process and the associated relationships between them;

[0021] The resource data corresponding to the input text is determined based on the association relationship between the matched associated phrase and the second keyword phrase.

[0022] Optionally, determining resource data corresponding to the input text based on the association relationship between the matched associated phrase and the second keyword phrase includes:

[0023] Assigning priorities to the matched associated phrases according to their length;

[0024] The association relationship between the matched associated word group and the second keyword group is obtained in descending order of priority, and resource data corresponding to the input text is determined based on the association relationship.

[0025] A second aspect of the present application provides a text matching device, comprising:

[0026] The acquisition module is used to obtain input text and resource data to be matched;

[0027] A word segmentation module is used to segment the input text and the resource data to be matched based on the words in the system vocabulary to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched;

[0028] The calculation module is used to calculate the similarity between the first keyword phrase and the second keyword phrase, and determine the resource data corresponding to the input text from the resource data to be matched according to the similarity.

[0029] A third aspect of the present application provides an electronic device, which includes a processor and a memory; wherein the memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the aforementioned text matching method.

[0030] A fourth aspect of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are loaded and executed by a processor, the aforementioned text matching method is implemented.

[0031] In this application, the input text and the resource data to be matched are first obtained; then, the input text and the resource data to be matched are segmented based on the words in the system vocabulary to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched; finally, the similarity between the first keyword phrase and the second keyword phrase is calculated, and the resource data corresponding to the input text is determined from the resource data to be matched based on the similarity. This application performs a segmentation operation with a finer matching granularity on the input text and the resource data to be matched based on the system vocabulary to obtain the corresponding first keyword phrase and second keyword phrase, so that the segmentation result is more accurate, and then the resource data in the resource data to be matched that matches the input text is determined by calculating the similarity between the first keyword phrase and the second keyword phrase, thereby improving the text matching accuracy in lightweight scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0033] Figure 1 A flow chart of a text matching method provided in this application;

[0034] Figure 2 A specific dictionary tree construction example diagram provided for this application;

[0035] Figure 3A schematic diagram of a specific text matching method provided in this application;

[0036] Figure 4 A schematic diagram of the structure of a text matching device provided in this application;

[0037] Figure 5 This is a structural diagram of a text matching electronic device provided in this application. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] Traditional input matching methods are based on comprehensive fuzzy matching of user-entered keywords. This results in data matching at a coarse granularity, and the vocabulary used for matching is relatively closed. This often leads to inaccurate matching results, and ideal matching resource data cannot be obtained. Furthermore, existing ElasticSearch technology uses an ES server for search matching. However, the need to build an ES server makes it suitable for business scenarios dedicated to search, such as heavyweight search engines. However, for scenarios requiring only a small and lightweight search tool, building an ES server is costly, and text matching resource utilization is low in lightweight scenarios. To address the above technical deficiencies, a text matching solution is provided. Based on the system vocabulary, a fine-grained word segmentation operation is performed on the input text and the resource data to be matched to obtain the corresponding first keyword phrase and second keyword phrase, making the word segmentation results more accurate. The similarity between the first keyword phrase and the second keyword phrase is then calculated to determine the resource data in the resource data to be matched that matches the input text, thereby improving text matching accuracy in lightweight scenarios.

[0040] Figure 1 This is a flow chart of a text matching method provided in an embodiment of the present application. Figure 1 As shown, the text matching method includes:

[0041] S11: Obtain input text and resource data to be matched.

[0042] In this embodiment, the input text and the resource data to be matched are first obtained. Simultaneously, a complete match is performed based on the user input, ensuring that the content is completely consistent with the user input. The input text is the search text, and the resource data to be matched is the search target. Resource data with a high degree of similarity to the input text is identified from the resource data to be matched. For example, if the resource data to be matched includes Computer A, Computer B, and Mobile Phone C, and the input text is "Computer," Computer A and Computer B are searched and output from the resource data to be matched.

[0043] S12: Segmenting the input text and the resource data to be matched based on the words in the system vocabulary to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched.

[0044] In this embodiment, the input text and the resource data to be matched are segmented based on the words in the system vocabulary to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched. Furthermore, the words in the system vocabulary are stored by constructing a dictionary tree to obtain a target dictionary tree corresponding to the system vocabulary. Then, the input text and the phrases in the resource data to be matched are matched respectively by traversing the target dictionary tree, so as to segment the input text and the resource data to be matched using the dictionary tree algorithm. That is, first, the data structure of the Trie tree of the system vocabulary is constructed, and it is used to segment the user input and the resource data to be matched to obtain keyword phrases. The user input is parsed by the word segmentation algorithm, and the granularity of the matching is relatively fine.

[0045] The dictionary tree, also known as the Trie tree, is a tree structure, also known as a word search tree, and is a variant of a hash tree. It is mostly used to count, sort and save a large number of strings (but not limited to strings), and is often used by search engine systems for text word frequency statistics. The Trie tree uses the common prefix of the string to reduce the query time, minimize unnecessary string comparisons, and has higher query efficiency than the hash tree. It can be understood that the construction and word segmentation process of the Trie tree are as follows: first, each word in the system vocabulary is stored in the form of one word and one node, and the following word is used as a child node of the previous word. The end node is marked to indicate the end of a word. For the same prefix, a common node is used to form a Trie tree. On this basis, the words to be segmented are matched word by word on the Trie tree. If there is a child node, the matching continues downward. If an identifier is encountered that indicates the end of a word, it is considered that a segmentation keyword is obtained, and the matching continues downward. For example Figure 2 The figure shows the construction process of a Trie tree.

[0046] For lightweight scenarios, first create a dynamic link library containing the Trie algorithm. Then, call the Trie algorithm from the dynamic link library to perform word segmentation on the input text and the matching resource data. Because the word segmentation algorithm is small and lightweight, there's no need to build a complex server. Instead, compile it as a class library, reference the dynamic link library DLL, and call the corresponding methods to perform word segmentation.

[0047] S13: Calculate the similarity between the first keyword phrase and the second keyword phrase, and determine the resource data corresponding to the input text from the resource data to be matched according to the similarity.

[0048] In this embodiment, the similarity between the first keyword phrase and the second keyword phrase is calculated, and based on the similarity, the resource data corresponding to the input text is determined from the resource data to be matched. Specifically, the cosine similarity algorithm can be used to calculate the similarity between the first keyword phrase and the second keyword phrase. The user-input keyword phrase and the resource data keyword phrase are similarly calculated based on the cosine similarity mathematical model to obtain the resource data with the highest similarity. Cosine similarity, also known as cosine similarity, evaluates the similarity between two vectors by calculating the cosine of the angle between them. Cosine similarity plots vectors into a vector space, such as the most common two-dimensional space, based on their coordinate values. Using the cosine similarity calculation algorithm, more accurate data resources are found and matched from the system. For lightweight scenarios, a dynamic link library containing the cosine similarity algorithm is first created. The cosine similarity algorithm is then called from the dynamic link library to calculate the similarity between the first keyword phrase and the second keyword phrase. Because the cosine similarity algorithm is small and lightweight, there is no need for complex server setup. Compiling it as a class library and then referencing the dynamic link library DLL allows the corresponding methods to be called to perform similarity calculations.

[0049] After calculating the similarity, determine whether the similarity is greater than a preset threshold value. If so, the resource data corresponding to the second keyword phrase whose similarity is greater than the preset threshold value is determined as the resource data corresponding to the input text. The preset threshold value is set according to business needs, and the embodiments of the present application do not limit this. Of course, in order to improve flexibility and fault tolerance, it is also possible to further determine whether the resource data corresponding to the second keyword phrase whose similarity is greater than the preset threshold value is determined as the resource data corresponding to the input text, and which keywords are determined as the resource data corresponding to the input text when multiple keywords are present in the second keyword phrase.

[0050] However, since the corresponding relationships in the existing text matching are relatively fixed and rigid, the words that fail to match cannot be expanded, and if a new matching corresponding relationship is added, a certain maintenance cost is required. Based on this, the embodiment of the present application can perform association matching when the similarity matching fails. When the similarity is less than the preset threshold, the first keyword phrase is matched with the words in the associated vocabulary to obtain an associated phrase that has an associated relationship with the first keyword phrase, wherein the associated vocabulary includes a plurality of word pairs that have an associated relationship in the historical matching process and the associated relationships between them. Vocabulary with an associated relationship is vocabulary with a corresponding relationship based on habit or convention. Finally, the resource data corresponding to the input text is determined based on the associated relationship between the associated phrase and the second keyword phrase. The specific process is as follows. Figure 3 shown.

[0051] In addition, in order to further improve the efficiency of association matching, priority is given to matching long association words. If a match fails, short association words are used. This can be achieved through priority. First, the matched association words are assigned a priority based on the length of the association word group. Then, the association relationship between the matched association word group and the second keyword word group is obtained in descending order of priority, and the resource data corresponding to the input text is determined based on the association relationship. For example, when the input text is a computer, the resource data corresponding to the input text matched through the above steps are "A computer + model" and "B computer". It is obvious that "A computer + model" is longer, so the resource data of "A computer + model" is judged first. In this way, more detailed and accurate resource data can be obtained. The association word library can be supplemented and improved based on each matching result. That is, for user input that cannot be matched, historical association data is recorded by manually specifying the match, and an association matching relationship is established to form a system association word library. For future matches, the matching rate can be improved by using the association word library when the word segmentation cannot be matched.

[0052] It can be seen that the embodiment of the present application first obtains the input text and the resource data to be matched; then, the input text and the resource data to be matched are segmented based on the words in the system vocabulary to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched; finally, the similarity between the first keyword phrase and the second keyword phrase is calculated, and the resource data corresponding to the input text is determined from the resource data to be matched based on the similarity. The embodiment of the present application performs a segmentation operation with a finer matching granularity on the input text and the resource data to be matched based on the system vocabulary to obtain the corresponding first keyword phrase and second keyword phrase, so that the segmentation result is more accurate, and then the resource data in the resource data to be matched that matches the input text is determined by calculating the similarity between the first keyword phrase and the second keyword phrase, thereby improving the text matching accuracy in lightweight scenarios.

[0053] See also Figure 4 As shown, the embodiment of the present application also discloses a text matching device, including:

[0054] Acquisition module 11, used to acquire input text and resource data to be matched;

[0055] A word segmentation module 12 is used to segment the input text and the resource data to be matched based on the words in the system vocabulary to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched;

[0056] The calculation module 13 is configured to calculate the similarity between the first keyword phrase and the second keyword phrase, and determine the resource data corresponding to the input text from the resource data to be matched according to the similarity.

[0057] It can be seen that the embodiment of the present application first obtains the input text and the resource data to be matched; then, the input text and the resource data to be matched are segmented based on the words in the system vocabulary to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched; finally, the similarity between the first keyword phrase and the second keyword phrase is calculated, and the resource data corresponding to the input text is determined from the resource data to be matched based on the similarity. The embodiment of the present application performs a segmentation operation with a finer matching granularity on the input text and the resource data to be matched based on the system vocabulary to obtain the corresponding first keyword phrase and second keyword phrase, so that the segmentation result is more accurate, and then the resource data in the resource data to be matched that matches the input text is determined by calculating the similarity between the first keyword phrase and the second keyword phrase, thereby improving the text matching accuracy in lightweight scenarios.

[0058] In some specific embodiments, the word segmentation module 12 specifically includes:

[0059] A construction unit, configured to store the words in the system vocabulary by constructing a dictionary tree to obtain a target dictionary tree corresponding to the system vocabulary;

[0060] The matching unit is used to match the phrases in the input text and the resource data to be matched respectively by traversing the target dictionary tree, so as to segment the input text and the resource data to be matched using the dictionary tree algorithm.

[0061] In some specific embodiments, the calculation module 13 specifically includes:

[0062] a similarity calculation unit, configured to calculate the similarity between the first keyword phrase and the second keyword phrase using a cosine similarity algorithm;

[0063] The judging unit is configured to judge whether the similarity is greater than a preset threshold, and if so, determine the resource data corresponding to the second keyword phrase with the similarity greater than the preset threshold as the resource data corresponding to the input text.

[0064] In some specific embodiments, the text matching device further includes:

[0065] an association matching module, configured to match the first keyword phrase with words in an association vocabulary if the similarities are all less than a preset threshold, to obtain an associated phrase associated with the first keyword phrase; wherein the association vocabulary includes a plurality of word pairs associated in historical matching processes and the association relationships therebetween;

[0066] an allocation module for assigning priorities to the matched associated phrases according to the length of the associated phrases;

[0067] The determination module is configured to obtain the association relationship between the matched associated word group and the second keyword group in descending order of priority, and determine the resource data corresponding to the input text according to the association relationship.

[0068] Furthermore, an embodiment of the present application also provides an electronic device. Figure 5 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram should not be considered as any limitation to the scope of application of the present application.

[0069] Figure 5 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the text matching method disclosed in any of the aforementioned embodiments.

[0070] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0071] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon may include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.

[0072] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, enabling the processor 21 to calculate and process the massive amount of data 223 in the memory 22. The operating system 221 can be Windows Server, NetWare, Unix, Linux, etc. In addition to including computer programs capable of implementing the text matching method performed by the electronic device 20 as disclosed in any of the aforementioned embodiments, the computer program 222 can further include computer programs capable of performing other specific tasks. The data 223 can include input text collected by the electronic device 20.

[0073] Furthermore, an embodiment of the present application also discloses a storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the text matching method steps disclosed in any of the aforementioned embodiments are implemented.

[0074] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0075] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0076] The above is a detailed introduction to the text matching method, device, equipment and storage medium provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A text matching method, characterized in that: include: Obtain input text and resource data to be matched; the input text is the search text, and the resource data to be matched is the search object; Segmenting the input text and the resource data to be matched based on words in a system vocabulary to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched; The word segmentation of the input text and the resource data to be matched based on the words in the system vocabulary includes: Storing the words in the system vocabulary by constructing a dictionary tree to obtain a target dictionary tree corresponding to the system vocabulary; Matching the input text and the phrases in the to-be-matched resource data respectively by traversing the target dictionary tree, so as to segment the input text and the to-be-matched resource data using a dictionary tree algorithm; The similarity between the first keyword phrase and the second keyword phrase is calculated, and resource data corresponding to the input text is determined from the resource data to be matched according to the similarity.

2. The text matching method according to claim 1, characterized in that The calculating the similarity between the first keyword phrase and the second keyword phrase includes: The similarity between the first keyword phrase and the second keyword phrase is calculated using a cosine similarity algorithm.

3. The text matching method according to claim 2, characterized in that Also includes: Creating a dynamic link library including the dictionary tree algorithm and the cosine similarity algorithm; The input text and the resource data to be matched are segmented or the similarity between the first keyword phrase and the second keyword phrase is calculated by calling a corresponding algorithm from the dynamic link library.

4. The text matching method according to any one of claims 1 to 3, characterized in that: Determining the resource data corresponding to the input text from the to-be-matched resource data according to the similarity includes: It is determined whether the similarity is greater than a preset threshold; if so, the resource data corresponding to the second keyword phrase with the similarity greater than the preset threshold is determined as the resource data corresponding to the input text.

5. The text matching method according to claim 4, characterized in that: After determining whether the similarity is greater than a preset threshold, the method further includes: If the similarities are all less than the preset threshold, matching the first keyword phrase with words in an associated vocabulary to obtain an associated vocabulary that has an associated relationship with the first keyword phrase; wherein the associated vocabulary includes a plurality of word pairs that have an associated relationship in a historical matching process and the associated relationships between them; The resource data corresponding to the input text is determined according to the association relationship between the matched associated phrase and the second keyword phrase.

6. The text matching method according to claim 5, characterized in that The determining of resource data corresponding to the input text based on the association relationship between the matched associated phrase and the second keyword phrase includes: assigning priorities to the matched associated phrases according to their lengths; The association relationship between the matched associated phrases and the second keyword phrases is obtained in descending order of priority, and resource data corresponding to the input text is determined based on the association relationship.

7. A text matching device, characterized in that: include: An acquisition module is used to acquire input text and resource data to be matched; the input text is the search text, and the resource data to be matched is the search object; A word segmentation module, configured to segment the input text and the resource data to be matched based on words in a system word library, to obtain a first keyword phrase corresponding to the input text and a second keyword phrase corresponding to the resource data to be matched; The word segmentation module specifically includes: A construction unit, configured to store the words in the system vocabulary by constructing a dictionary tree to obtain a target dictionary tree corresponding to the system vocabulary; A matching unit, configured to match the input text and the phrases in the resource data to be matched respectively by traversing the target dictionary tree, so as to segment the input text and the resource data to be matched using a dictionary tree algorithm; The calculation module is configured to calculate the similarity between the first keyword phrase and the second keyword phrase, and determine the resource data corresponding to the input text from the resource data to be matched according to the similarity.

8. An electronic device, characterized in that: The electronic device includes a processor and a memory; wherein the memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the text matching method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that Used to store computer-executable instructions, which, when loaded and executed by a processor, implement the text matching method according to any one of claims 1 to 6.

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