WO2020052059A1 - Method and apparatus for generating information - Google Patents

Abstract

A method and apparatus for generating information. The method comprises: obtaining at least one search term set (201); for a target search term in a target search term set, determining whether a preset term set comprises a corresponding term having a pre-established correspondence with the target search term; determining, in response to determining that at least one corresponding term is comprised, a degree of similarity of the corresponding term in the at least one corresponding term to the target search term; extracting a target number of corresponding terms from at least one corresponding term as a set of corresponding terms corresponding to the target search term in order of magnitude of similarity (202); generating at least one search term set on the basis of the obtained corresponding term set (203). The method facilitates improving the comprehensiveness and pertinence of information searching.

Description

Method and device for generating information

This patent application requires a Chinese patent application filed on September 14, 2018, with application number 201811075006.X, the applicant being Beijing BYTE Network Technology Co., Ltd., and the invention name is "Method and Device for Generating Information" Priority is hereby incorporated by reference in its entirety.

Technical field

Embodiments of the present application relate to the field of computer technology, and in particular, to a method and an apparatus for generating information.

Background technique

At present, when a user searches for information on the Internet, the search term input by the user is usually used for the search, and the search results include words that are the same as or similar to the search term. The accuracy of the search results depends on the matching rules when matching the search words entered by the user with the information in the network.

Summary of the Invention

The embodiments of the present application provide a method and device for generating information.

In a first aspect, an embodiment of the present application provides a method for generating information. The method includes: obtaining a target search term set; and for a target search term in the target search term set, determining whether a preset word set includes the same term as the A corresponding term having a pre-established correspondence relationship for the target search term; in response to determining that at least one corresponding term is included, determining the similarity between the corresponding term in the at least one corresponding term and the target search term; A target number of corresponding words are extracted from the corresponding words as a corresponding word set corresponding to the target search word; based on the obtained corresponding word set, at least one search word set is generated.

In some embodiments, after generating at least one search term set based on the obtained corresponding word set, the method further includes: for the search term set in the at least one search term set, using the search terms included in the search term set for information Search, get search results and output.

In some embodiments, the target search term set is a set of words obtained by segmenting a search sentence entered by a user.

In some embodiments, the preset word set includes at least one sub-set; and determining whether the preset word set includes a corresponding word having a pre-established correspondence relationship with the target search term includes determining whether the at least one sub-set is There is a subset including the target search term; in response to determining existence, it is determined that other words in the subset including the target search term other than the target search term are corresponding words corresponding to the target search term.

In some embodiments, at least one sub-set included in the preset word set is obtained according to the following steps in advance: obtaining a target text set; performing word segmentation on the target text in the target text set to obtain a word set; The words in the word set are clustered with synonyms to obtain at least one sub-set. For the sub-set in the at least one sub-set, the similarity between the two words included in the sub-set is greater than or equal to a preset similarity threshold.

In some embodiments, at least one subset of the preset word set is obtained in advance according to the following steps: obtaining the original search term set; and for the original search term in the original search term set, inputting the original search term into the preset search The engine obtains at least one search result; from the at least one search result, extracts a word with a set characteristic as a target word; and based on the extracted target word and the original search word, generates a sub-set included in the word set.

In a second aspect, an embodiment of the present application provides an apparatus for generating information. The apparatus includes: an obtaining unit configured to obtain a target search term set; and an extracting unit configured to perform a target search on the target search term set. Words, determine whether the preset word set includes corresponding words that have a pre-established correspondence relationship with the target search word; and in response to determining that it includes at least one corresponding word, determine that the corresponding word in the at least one corresponding word is similar to the target search word Degree; in accordance with the order of the degree of similarity, extract a target number of corresponding words from at least one corresponding word as a corresponding word set corresponding to the target search term; a generating unit configured to generate at least one based on the obtained corresponding word set Search term collection.

In some embodiments, the apparatus further includes: a search unit configured to perform a search of information on the search term set in the at least one search term set using the search terms included in the search term set to obtain a search result and an output.

In some embodiments, the target search term set is a set of words obtained by segmenting a search sentence entered by a user.

In some embodiments, the preset word set includes at least one sub-set; and the extraction unit includes: a first determination module configured to determine whether a sub-set including the target search word exists in the at least one sub-set; a second determination A module configured to, in response to determining existence, determine that other words in the subset including the target search term other than the target search term are corresponding words corresponding to the target search term.

In some embodiments, at least one sub-set included in the preset word set is obtained according to the following steps in advance: obtaining a target text set; performing word segmentation on the target text in the target text set to obtain a word set; The words in the word set are clustered with synonyms to obtain at least one sub-set. For the sub-set in the at least one sub-set, the similarity between the words included in the sub-set is greater than or equal to a preset similarity threshold.

In some embodiments, at least one subset of the preset word set is obtained in advance according to the following steps: obtaining the original search term set; and for the original search term in the original search term set, inputting the original search term into the preset search The engine obtains at least one search result; from the at least one search result, extracts a word with a set characteristic as a target word; and based on the extracted target word and the original search word, generates a sub-set included in the word set.

According to a third aspect, an embodiment of the present application provides a server. The server includes: one or more processors; a storage device on which one or more programs are stored; and when one or more programs are processed by one or more The processor executes such that one or more processors implement the method as described in any implementation of the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer-readable medium having stored thereon a computer program that, when executed by a processor, implements the method as described in any implementation manner of the first aspect.

The method and device for generating information provided in the embodiments of the present application obtain a target search term set, and then, for a target search term in the target search term set, determine at least a preset word set corresponding to the target search term from the preset word set. A corresponding word, and according to the order of the similarity between the corresponding word and the target search word, a target number of corresponding words are extracted from at least one corresponding word as a corresponding word set corresponding to the target search word, and finally based on the obtained each Corresponding to the word set, at least one search word set is generated, so that more search word sets can be generated according to the target search word set, which helps to improve the comprehensiveness and pertinence of the information search.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features, objects, and advantages of the present application will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

FIG. 1 is an exemplary system architecture diagram to which an embodiment of the present application can be applied; FIG.

2 is a flowchart of an embodiment of a method for generating information according to an embodiment of the present application;

3 is a schematic diagram of an application scenario of a method for generating information according to an embodiment of the present application;

4 is a flowchart of still another embodiment of a method for generating information according to an embodiment of the present application;

5 is a schematic structural diagram of an embodiment of an apparatus for generating information according to an embodiment of the present application;

FIG. 6 is a schematic structural diagram of a computer system suitable for implementing a server according to an embodiment of the present application.

detailed description

The following describes the present application in detail with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention, rather than limiting the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The application will be described in detail below with reference to the drawings and embodiments.

FIG. 1 illustrates an exemplary system architecture 100 to which a method for generating information or a device for generating information according to an embodiment of the present application can be applied.

As shown in FIG. 1, the system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. The network 104 is a medium for providing a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, and so on.

The user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages and the like. Various communication client applications, such as search applications, web browser applications, and shopping applications, can be installed on the terminal devices 101, 102, and 103.

The terminal devices 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, and 103 are hardware, they can be various electronic devices, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Pictures Experts Group Audio Layer III, Motion Picture Expert Compression Standard Audio Level 3), MP4 (Moving Picture Experts Group Audio Audio Layer IV, Motion Picture Expert Compression Standard Audio Level 4) player, laptop portable computer and desktop computer, etc. When the terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules (such as software or software modules used to provide distributed services), or it can be implemented as a single software or software module. It is not specifically limited here.

The server 105 may be a server that provides various services, such as a background information processing server that processes a target search word set sent by the terminal devices 101, 102, and 103. The background information processing server may process the obtained target search term set and generate a processing result (for example, at least one search term set).

It should be noted that the method for generating information provided by the embodiments of the present application is generally executed by the server 105, and accordingly, the apparatus for generating information is generally set in the server 105.

It should be noted that the server may be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers or as a single server. When the server is software, it can be implemented as multiple software or software modules (such as software or software modules used to provide distributed services), or it can be implemented as a single software or software module. It is not specifically limited here.

It should be understood that the numbers of terminal devices, networks, and servers in FIG. 1 are merely exemplary. According to implementation needs, there can be any number of terminal devices, networks, and servers.

With continued reference to FIG. 2, a flowchart 200 of one embodiment of a method for generating information according to the present application is shown. The method for generating information includes the following steps:

Step 201: Obtain a target search term set.

In this embodiment, an execution subject of the method for generating information (for example, a server shown in FIG. 1) may obtain a target search word set from a remote or local source through a wired connection method or a wireless connection method. The target search word set may be a set of words to be used for information search. For example, the target search word set may be a set of words for searching information input by a user using a terminal device (such as the terminal device shown in FIG. 1) and sent to the execution subject.

In some optional implementation manners of this embodiment, the target search term set may be a set of words obtained by segmenting a search sentence input by a user. Specifically, the user may use a terminal device as shown in FIG. 1 to input a search sentence and send the search sentence to the above-mentioned execution subject, and the above-mentioned execution subject may perform word cutting on the sentence according to an existing method (for example, the maximum forward matching method) , N-gram model method, Hidden Markov model method, etc.), perform word segmentation on the search sentence to obtain the target search word set.

Step 202: For a target search term in the target search term set, determine whether a preset word set includes a corresponding term having a pre-established correspondence relationship with the target search term; and in response to determining that it includes at least one corresponding term, determine at least one corresponding term. The similarity between the corresponding words in the words and the target search word; according to the order of the similarity, a target number of corresponding words are extracted from at least one corresponding word as the corresponding word set corresponding to the target search word.

In this embodiment, for the target search term in the target search term set, the execution body may perform the following steps:

In step 2021, it is determined whether the preset word set includes a corresponding word that has a pre-established correspondence relationship with the target search word.

Specifically, the word set may be set in the execution subject in advance, or may be set in other electronic devices in communication with the execution subject in advance. The correspondence between the target search word and the corresponding word can be established in various ways in advance. For example, the correspondence between the target search word and the corresponding word can be characterized by a pre-established two-dimensional table, and the correspondence between the target search word and the corresponding word can be established in advance through key (value) value pairs, linked lists, and other forms. relationship.

In some optional implementation manners of this embodiment, the preset word set includes at least one sub-set. The above-mentioned execution subject may determine whether the word set includes a corresponding word that has a pre-established correspondence relationship with the target search term according to the following steps:

First, it is determined whether a sub-set including the target search word exists in at least one sub-set included in the preset word set. Then, in response to determining the existence, it is determined that the other words in the subset including the target search term other than the target search term are corresponding words corresponding to the target search term.

Generally, the similarity between the words in the subset can be greater than or equal to a preset similarity threshold, that is, the words in the subset are synonyms or synonyms. As an example, assume that the target search term is "kids", and a certain subset includes the following words: children, kids, teenagers, children. The words "child", "juvenile", and "child" in the sub-collection are corresponding words to the target search term "kid". It should be noted that the similarity between the words in the sub-set may be calculated by the above-mentioned execution subject or other electronic devices in advance by using various existing algorithms for calculating the similarity between the words.

In some optional implementations of this embodiment, at least a subset of the preset word set may be obtained in advance by the execution subject or other electronic device according to the following steps:

First, get the target text collection. The target text may be the text to be segmented. The target text set may be stored in the above-mentioned execution body, or may be stored in other electronic devices that are communicatively connected with the above-mentioned execution body. It should be noted that the target text in the target text set can be stored separately in an electronic device, and the target text in the target text set can also be stored in an electronic device cluster composed of multiple electronic devices.

Then, the target text in the target text set is cut to obtain a word set. Specifically, the foregoing execution subject or other electronic device may perform word segmentation according to various existing methods for word segmentation, and perform word segmentation on each sentence included in the target text to obtain a word set.

Finally, the words in the word set obtained after the word segmentation are clustered in a synonym to obtain at least one subset. For a sub-set in at least one sub-set, the similarity between the words included in the sub-set is greater than or equal to a preset similarity threshold. Specifically, the execution subject or other electronic device may use the existing synonym clustering algorithm to perform clustering of the synonyms in the words in the word set to obtain at least one subset. As an example, the above-mentioned execution subject or other electronic device may use an existing word vector model (for example, word2vec, sense2vec, etc.) to obtain the word vector of each word in the word set. Then use the algorithm to cluster the word vectors to cluster each word vector (such as k-means algorithm, decision tree algorithm, etc.), and the similarity between the pair of word vectors included in each cluster ( For example, the similarity may be Euclidean distance between vectors, cosine distance, etc.) is greater than or equal to a preset similarity threshold. Thus, the obtained set of words represented by the word vectors included in each cluster is determined as a subset included in the word set.

In some optional implementations of this embodiment, at least a subset of the preset word set may also be obtained in advance by the foregoing execution subject or other electronic device according to the following steps:

First, get the original set of search terms. The original search term may be a search term previously input by a technician.

Then, for the original search terms in the original search term set, perform the following sub-steps:

Sub-step 1. Enter the original search term into a preset search engine to obtain at least one search result. The search engine mentioned above refers to collecting information from the Internet according to a certain strategy and using a specific computer program. After the information is organized and processed, it provides a search service for users and displays the information related to the user's search to The user's system. The preset search engine may be various existing search engines. The search result in the at least one search result may include text, and the text may include a title, a body text, and the like.

Sub-step 2: extract at least one search word as a target word from at least one search result. Specifically, the set feature may be various features of a word, including, but not limited to, at least one of the following: a font color is a preset color, a background color of highlighted text is a preset color, and the like. As an example, the text in the search results may include text in red font, and the text in red font is usually a word with the same meaning or similar meaning to the search word. The execution subject or other electronic device can identify features such as the color of the text in the search results, thereby extracting words with set features as target words.

Sub-step three: Based on the extracted target word and the original search word, a sub-set included in the word set is generated. As an example, the number of the extracted target words may be at least one, and the execution subject or other electronic device may form the original search word and the extracted target words into a subset. As another example, the execution subject or other electronic device may calculate the similarity between the original search word and each target word, and combine the target word corresponding to the similarity greater than or equal to a preset similarity threshold with the original search word into a subset. .

In step 2022, in response to determining that it includes at least one corresponding word, a similarity between the corresponding word in the at least one corresponding word and the target search word is determined.

Specifically, the above-mentioned execution body may use various existing algorithms for calculating the similarity between words (such as the Levenshtein Distance algorithm, the cosine distance algorithm based on the Vector Space Model (VSM), etc.), Determine the similarity between the corresponding word in the at least one corresponding word and the target search word.

Step 2023: According to the order of the similarity, a target number of corresponding words are extracted from the at least one corresponding word as a corresponding word set corresponding to the target search word.

Specifically, the above-mentioned execution subject may extract a target number of corresponding words from at least one corresponding word in the order of similarity from large to small as a corresponding word set corresponding to the target search word. The target number may be a preset number or a number determined according to a number of corresponding words included in at least one corresponding word corresponding to the target search term. For example, when the number of corresponding words included in the at least one corresponding word is greater than or equal to the preset number, the target number is the preset number; otherwise, the target number is the number of corresponding words included in the at least one corresponding word.

In practice, the corresponding words corresponding to the target search term may be synonyms or synonyms of the target search term. The corresponding word sets extracted according to the order of similarity can help to generate the final search word set in a targeted manner.

Step 203: Generate at least one search word set based on the obtained corresponding word set.

In this embodiment, the above-mentioned execution subject may generate at least one search word set based on the obtained corresponding word set. As an example, the foregoing execution subject may combine each of the obtained corresponding word sets and the target search term set into a search term set.

As another example, the execution body may extract a corresponding word from each of the obtained corresponding word sets according to the order of the similarity between the corresponding word and the corresponding target search word, or randomly extract a corresponding word, respectively. The extracted corresponding words and target search words in the target search word set that do not have corresponding corresponding words are combined into a search word set.

With continued reference to FIG. 3, FIG. 3 is a schematic diagram of an application scenario of the method for generating information according to this embodiment. In the application scenario of FIG. 3, the server 301 first obtains a target search word set 303 (for example, including a target search term “boy” and “urine is not wet”) input by the user using the terminal device 302. Then, the server 301 determines, from the preset word set 304, at least one corresponding word 3041 corresponding to the target search term "boy" includes: "child", "baby", "juvenile"; determines the target search term "urine not wet" The corresponding at least one corresponding word 3042 includes: "diaper", "urine mustard". Then, the server 301 extracts, from at least one corresponding word corresponding to each target search word, two corresponding words as the corresponding words corresponding to the search word in the order of the similarity between the corresponding word and the corresponding target search word. set. The corresponding word set 305 corresponding to the target search term “boy” includes: “child” and “baby”, and the corresponding word set 306 corresponding to the target search term “urine not wet” includes: “diaper” and “urine mustard”. Then, the server 301 generates two search word sets 307 (for example, including “children” and “diapers”) and 308 (for example, “baby” and “urinary mustard”) based on the obtained corresponding word sets 305 and 306.

The method provided by the above embodiments of the present application, by obtaining a target search term set, and for the target search term in the target search term set, determining at least one corresponding word corresponding to the target search term from a preset word set, and According to the order of the similarity between the corresponding word and the target search word, a target number of corresponding words are extracted from at least one corresponding word as the corresponding word set corresponding to the target search word, and finally based on the obtained corresponding word set, generate At least one search term set, so that more search term sets can be generated according to the target search term set, which helps to improve the comprehensiveness and pertinence of the information search.

With further reference to FIG. 4, a flowchart 400 of yet another embodiment of a method for generating information is shown. The process 400 of the method for generating information includes the following steps:

Step 401: Obtain a target search term set.

In this embodiment, step 401 is substantially the same as step 201 in the embodiment corresponding to FIG. 2, and details are not described herein again.

Step 402: For a target search term in the target search term set, determine whether a preset word set includes a corresponding term having a pre-established correspondence relationship with the target search term; and in response to determining that it includes at least one corresponding term, determine at least one corresponding term. The similarity between the corresponding words in the words and the target search word; according to the order of the similarity, a target number of corresponding words are extracted from at least one corresponding word as the corresponding word set corresponding to the target search word.

In this embodiment, step 402 is substantially the same as step 202 in the embodiment corresponding to FIG. 2, and details are not described herein again.

Step 403: Generate at least one search word set based on the obtained corresponding word set.

In this embodiment, step 403 is substantially the same as step 203 in the embodiment corresponding to FIG. 2, and details are not described herein again.

Step 404: For at least one search term set in the search term set, use the search terms included in the search term set to perform an information search to obtain a search result and an output.

In this embodiment, for the search term set in the at least one search term set obtained in step 403, the execution subject of the method for generating information (for example, the server shown in FIG. 1) may use the search included in the search term set. Word search for information, get search results and output.

Specifically, the execution body may input search terms in a search term set into a preset search engine to obtain search results. Alternatively, the execution body may use the search words in the search word set to perform information search in a preset information set (for example, a set of information included in a website) to obtain a search result. The search results may include, but are not limited to, at least one of the following: pictures, text, links, and the like. The search results can be output in various ways, for example, the search results can be displayed on a display connected to the execution subject, or the search results can be sent to a terminal device communicatively connected to the execution subject.

It should be noted that the number of search results corresponding to the search term set may be a number preset by a technician, so that the search results can be more targeted.

As can be seen from FIG. 4, compared with the embodiment corresponding to FIG. 2, the process 400 of the method for generating information in this embodiment highlights the steps of searching and outputting search results using the generated at least one search term set. . Therefore, the solution described in this embodiment can use the generated search word set to obtain more comprehensive and targeted search results.

With further reference to FIG. 5, as an implementation of the methods shown in the foregoing figures, this application provides an embodiment of an apparatus for generating information. The apparatus embodiment corresponds to the method embodiment shown in FIG. 2. The device can be specifically applied to various electronic devices.

As shown in FIG. 5, the apparatus 500 for generating information in this embodiment includes: an obtaining unit 501 configured to obtain a target search term set; and an extraction unit 502 configured to perform a target search term in the target search term set, Determining whether the preset word set includes a corresponding word having a pre-established correspondence relationship with the target search word; and in response to determining that it includes at least one corresponding word, determining a similarity between the corresponding word in the at least one corresponding word and the target search word; According to the order of the degree of similarity, a target number of corresponding words are extracted from at least one corresponding word as a corresponding word set corresponding to the target search word; a generating unit 503 is configured to generate at least one search based on the obtained corresponding word set Word collection.

In this embodiment, the obtaining unit 501 may obtain the target search word set from a remote or local source through a wired connection method or a wireless connection method. The target search word set may be a set of words to be used for information search. For example, the target search word set may be a set of words for searching information input by a user using a terminal device (such as the terminal device shown in FIG. 1) and sent to the above-mentioned apparatus 500.

In this embodiment, for the target search term in the target search term set, the above-mentioned extraction unit 502 may perform the following steps:

Step 5021: Determine whether the preset word set includes a corresponding word that has a pre-established correspondence relationship with the target search word.

Specifically, the word set may be set in the device 500 in advance, or may be set in other electronic equipment in communication with the device 500 in advance. The correspondence between the target search word and the corresponding word can be established in various ways in advance. For example, the correspondence between the target search word and the corresponding word can be characterized by a pre-established two-dimensional table, and the correspondence between the target search word and the corresponding word can be established in advance through key (value) value pairs, linked lists, and other forms. Correspondence relationship.

Step 5022: In response to determining that the at least one corresponding word is included, determine the similarity between the corresponding word in the at least one corresponding word and the target search word.

Specifically, the above-mentioned extraction unit 502 may follow various existing algorithms for calculating the similarity between words (for example, the Levenshtein Distance algorithm, the cosine distance algorithm based on the Vector Space Model (VSM), etc.) To determine the similarity between the corresponding word in the at least one corresponding word and the target search word.

Step 5023: According to the order of the degree of similarity, a target number of corresponding words are extracted from at least one corresponding word as a corresponding word set corresponding to the target search word.

Specifically, the above-mentioned execution subject may extract a target number of corresponding words from at least one corresponding word in the order of similarity from large to small as a corresponding word set corresponding to the target search word. The target number may be a preset number or a number determined according to a number of corresponding words included in at least one corresponding word corresponding to the target search term. For example, when the number of corresponding words included in the at least one corresponding word is greater than or equal to the preset number, the target number is the preset number; otherwise, the target number is the number of corresponding words included in the at least one corresponding word.

In practice, the corresponding words corresponding to the target search term may be synonyms or synonyms of the target search term. The corresponding word sets extracted according to the order of similarity can help to generate the final search word set in a targeted manner.

In this embodiment, the generating unit 503 may generate at least one search word set based on the obtained corresponding word set. As an example, the foregoing generating unit 503 may combine each of the obtained corresponding word sets and the target search word set into a search word set.

As another example, the generating unit 503 may extract a corresponding word from each of the obtained corresponding word sets in the order of the similarity between the corresponding word and the corresponding target search word, or randomly extract each of the extracted words. A combination of a corresponding search term and a target search term in the target search term set that does not have a corresponding corresponding term is a search term set.

In some optional implementations of this embodiment, the apparatus 500 may further include: a search unit (not shown in the figure) configured to use the search word set for at least one search word set in the search word set Includes search terms for information search to get search results and output.

In some optional implementation manners of this embodiment, the target search term set is a set of words obtained by segmenting a search sentence input by a user.

In some optional implementations of this embodiment, the preset word set includes at least one sub-set; and the extraction unit 502 may include a first determination module (not shown in the figure) configured to determine at least one sub-set Whether a sub-set including the target search term exists in the set; a second determination module (not shown in the figure) configured to determine, in response to the determination of existence, to exclude the target search term from the sub-set including the target search term Other words are corresponding words corresponding to the target search term.

In some optional implementations of this embodiment, at least one sub-set included in the preset word set may be obtained in advance according to the following steps: obtaining a target text set; cutting the target text in the target text set to obtain a word Set; synonym clustering of the words in the word set obtained after cutting the words to obtain at least one subset, wherein, for the subset in the at least one subset, the similarity between the two words included in the subset is greater than Equal to a preset similarity threshold.

In some optional implementations of this embodiment, at least one sub-set included in the preset word set may be obtained in advance according to the following steps: obtaining the original search term set; for the original search term in the original search term set, The original search term is input into a preset search engine to obtain at least one search result. From the at least one search result, a word having a set feature is extracted as a target word. Based on the extracted target word and the original search word, generating a word set includes Child collection.

The apparatus provided by the foregoing embodiment of the present application obtains a target search term set, and then, for a target search term in the target search term set, determines at least one corresponding word corresponding to the target search term from a preset word set, and According to the order of the similarity between the corresponding word and the target search word, a target number of corresponding words are extracted from at least one corresponding word as the corresponding word set corresponding to the target search word, and finally based on the obtained corresponding word set, generate At least one search term set, so that more search term sets can be generated according to the target search term set, which helps to improve the comprehensiveness and pertinence of the information search.

Reference is now made to FIG. 6, which shows a schematic structural diagram of a computer system 600 suitable for implementing a server according to an embodiment of the present application. The server shown in FIG. 6 is only an example, and should not impose any limitation on the functions and scope of use of the embodiments of the present application.

As shown in FIG. 6, the computer system 600 includes a central processing unit (CPU) 601, which can be loaded into a random access memory (RAM) 603 according to a program stored in a read-only memory (ROM) 602 or from a storage portion 608. Instead, perform various appropriate actions and processes. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

The following components are connected to the I / O interface 605: an input portion 606 including a keyboard, a mouse, etc .; an output portion 607 including a liquid crystal display (LCD), etc .; a speaker, etc .; a storage portion 608 including a hard disk, etc .; A communication section 609 of a network interface card such as a modem. The communication section 609 performs communication processing via a network such as the Internet. The driver 610 is also connected to the I / O interface 605 as necessary. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product including a computer program carried on a computer-readable medium, the computer program containing program code for performing a method shown in a flowchart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and / or installed from a removable medium 611. When the computer program is executed by a central processing unit (CPU) 601, the above-mentioned functions defined in the method of the present application are executed.

It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable medium or any combination of the foregoing. The computer-readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable Read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing. In this application, a computer-readable medium may be any tangible medium that contains or stores a program that can be used by or in combination with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal that is included in baseband or propagated as part of a carrier wave, and which carries computer-readable program code. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for performing the operations of the present application may be written in one or more programming languages, or combinations thereof, including programming languages such as Java, Smalltalk, C ++, and also conventional Procedural programming language—such as "C" or a similar programming language. The program code can be executed entirely on the user's computer, partly on the user's computer, as an independent software package, partly on the user's computer, partly on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider) Internet connection).

The flowchart and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, which contains one or more functions to implement a specified logical function Executable instructions. It should also be noted that in some alternative implementations, the functions noted in the blocks may also occur in a different order than those marked in the drawings. For example, two successively represented boxes may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified function or operation , Or it can be implemented with a combination of dedicated hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software or hardware. The described unit may also be provided in a processor, for example, it may be described as: a processor includes an obtaining unit, an extracting unit, and a generating unit 503. Wherein, the names of these units do not constitute a limitation on the unit itself in some cases, for example, the obtaining unit may also be described as "a unit for obtaining a set of target search words".

As another aspect, the present application also provides a computer-readable medium, which may be included in the server described in the above embodiments; or may exist alone without being assembled into the server. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the server, the server is caused to: obtain a target search term set; determine a preset for the target search term in the target search term set Whether the set of words includes corresponding words that have a pre-established correspondence relationship with the target search term; in response to determining to include at least one corresponding word, determine the similarity between the corresponding word in the at least one corresponding word and the target search term; according to the similarity In order of size, extract a target number of corresponding words from at least one corresponding word as a corresponding word set corresponding to the target search term; and generate at least one search word set based on the obtained corresponding word set.

The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solution of the specific combination of the above technical features, but it should also cover the above technical features or Other technical solutions formed by arbitrarily combining their equivalent features. For example, a technical solution formed by replacing the above features with technical features disclosed in the present application (but not limited to) with similar functions.

Claims (14)

A method for generating information, including: Get the target search term set; For a target search term in the target search term set, determine whether a preset word set includes a corresponding term having a pre-established correspondence relationship with the target search term; and in response to determining that it includes at least one corresponding term, determine at least one corresponding term The similarity between the corresponding word in the target search word and the target search word; in accordance with the order of the similarity, extract a target number of corresponding words from at least one corresponding word as the corresponding word set corresponding to the target search word; Based on the obtained corresponding word set, at least one search word set is generated. The method according to claim 1, wherein after the generating at least one search word set based on the obtained corresponding word set, the method further comprises: For a search term set in the at least one search term set, an information search is performed using the search terms included in the search term set to obtain a search result and an output. The method according to claim 1, wherein the target search term set is a set of words obtained by segmenting a search sentence input by a user. The method according to any one of claims 1-3, wherein the preset word set includes at least one sub-set; and The determining whether the preset word set includes corresponding words having a pre-established correspondence relationship with the target search word includes: Determining whether a sub-set including the target search term exists in the at least one sub-set; In response to determining that it exists, it is determined that other words in the subset including the target search term other than the target search term are corresponding words corresponding to the target search term. The method according to claim 4, wherein the at least one subset included in the preset word set is obtained in advance according to the following steps: Get the target text collection; Perform word segmentation on the target text in the target text set to obtain a word set; Synonym clustering is performed on the words in the word set obtained after the word segmentation to obtain at least one sub-set, and for the sub-set in the at least one sub-set, the similarity between the words included in the sub-set is greater than Equal to a preset similarity threshold. The method according to claim 4, wherein the at least one subset included in the preset word set is obtained in advance according to the following steps: Get the original search term set; For the original search term in the original search term set, input the original search term into a preset search engine to obtain at least one search result; and from the at least one search result, extract a word having a set characteristic as a target word Generating a sub-set included in the word set based on the extracted target word and the original search term. An apparatus for generating information includes: An obtaining unit configured to obtain a target search term set; An extraction unit configured to determine, for a target search term in the target search term set, whether a preset word set includes a corresponding term having a pre-established correspondence relationship with the target search term; and in response to determining to include at least one corresponding term To determine the similarity between the corresponding word in the at least one corresponding word and the target search word; in accordance with the order of the similarity, extract the target number of corresponding words from the at least one corresponding word as the corresponding word set corresponding to the target search word; The generating unit is configured to generate at least one search word set based on the obtained corresponding word set. The apparatus according to claim 7, wherein the apparatus further comprises: The search unit is configured to perform an information search on the search term set in the at least one search term set using the search terms included in the search term set to obtain a search result and an output. The apparatus according to claim 7, wherein the target search term set is a set of words obtained by segmenting a search sentence input by a user. The apparatus according to one of claims 7-9, wherein the preset word set includes at least one sub-set; and The extraction unit includes: A first determining module configured to determine whether a sub-set including the target search term exists in the at least one sub-set; The second determination module is configured to determine, in response to the determination of existence, that other words in the subset including the target search term other than the target search term are corresponding words corresponding to the target search term. The apparatus according to claim 10, wherein at least one subset included in the preset word set is obtained in advance according to the following steps: Get the target text collection; Perform word segmentation on the target text in the target text set to obtain a word set; Synonym clustering is performed on the words in the word set obtained after the word segmentation to obtain at least one sub-set, and for the sub-set in the at least one sub-set, the similarity between the two words included in the sub-set is greater than Equal to a preset similarity threshold. The apparatus according to claim 10, wherein at least one subset included in the preset word set is obtained in advance according to the following steps: Get the original search term set; For the original search term in the original search term set, input the original search term into a preset search engine to obtain at least one search result; and from the at least one search result, extract a word having a set characteristic as a target word Generating a sub-set included in the word set based on the extracted target word and the original search term. A server including: One or more processors; A storage device on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-6. A computer-readable medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the method of any one of claims 1-6.

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