CN110555105A - Object processing method and system, computer system and computer readable storage medium - Google Patents
Object processing method and system, computer system and computer readable storage medium Download PDFInfo
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Abstract
The present disclosure provides an object processing method, including: obtaining corpus information associated with an object; determining one or more characteristic words contained in the corpus information associated with the object, wherein each characteristic word corresponds to a characteristic word weight; quantifying corpus information associated with the object based on the feature word weight corresponding to each feature word in the one or more feature words; and determining whether the object has a predetermined attribute according to a quantization result of the corpus information associated with the object. The present disclosure also provides an object processing system, a computer system, and a computer-readable storage medium.
Description
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to an object processing method and system, a computer system, and a computer-readable storage medium.
background
In the related art, as soon as a brand owner has a new product, the brand owner generally takes a new product release meeting or carries out product publicity through a network. In addition, for some small and popular brands, related information can be issued through an online channel for product promotion, for example, related information of a new product is promoted on platforms such as official websites, official microblogs and WeChat public numbers. And how to identify whether the related information released on different platforms includes the related information of a new product in time is of great significance to product selection.
In order to identify new products in time, in the related art, clustering analysis is generally performed on articles (or only a small piece of description information) about products, the articles are divided into a plurality of clusters according to the product categories, and for a new article, if the article is found to be far away from each cluster center, the product described in the article is considered to be a new product.
however, in implementing the concept of the present disclosure, the inventors found that at least the following problems exist in the related art:
the articles which are irrelevant to the products can be mixed in the product clustering analysis of the articles, so that the accuracy of identifying the new products is low.
Disclosure of Invention
in view of the above, the present disclosure provides an object processing method and system, a computer system, and a computer-readable storage medium.
one aspect of the present disclosure provides an object processing method, including obtaining corpus information associated with an object; determining one or more feature words contained in the corpus information associated with the object, wherein each feature word corresponds to a feature word weight; quantizing the corpus information associated with the object based on a feature word weight corresponding to each of the one or more feature words; and determining whether the object has a predetermined attribute according to a quantization result of the corpus information associated with the object.
According to an embodiment of the present disclosure, the method further includes determining whether one or more feature words are included in the corpus information associated with the object before determining the one or more feature words included in the corpus information associated with the object; and determining that the object does not have the predetermined attribute when it is determined that the corpus information associated with the object does not include the one or more feature words.
according to an embodiment of the present disclosure, determining whether the object has a predetermined attribute according to a quantization result of the corpus information associated with the object includes determining whether a quantization value corresponding to the quantization result of the corpus information associated with the object is greater than or equal to a preset threshold; and determining that the object has the predetermined attribute when it is determined that a quantization value corresponding to a quantization result of the corpus information associated with the object is greater than or equal to the preset threshold.
According to an embodiment of the present disclosure, the object processing method further includes determining a weight of each of the one or more feature words, and determining the weight of each of the one or more feature words includes obtaining a training sample, where the training sample is used to determine the weight of each of the one or more feature words; classifying the training samples to obtain a first data set and a second data set, wherein the object corresponding to the first data set has the predetermined attribute, and the object corresponding to the second data set does not have the predetermined attribute; determining the proportion of each feature word in the one or more feature words to all words contained in the first data set to obtain a first proportion; determining the proportion of each feature word in the one or more feature words to all words contained in the second data set to obtain a second proportion; and determining the characteristic word weight of each characteristic word in the one or more characteristic words according to the first ratio and the second ratio.
According to an embodiment of the present disclosure, determining one or more feature words included in the corpus information associated with the object includes performing word segmentation processing on the corpus information associated with the object to obtain one or more words; judging whether the words in the one or more words are the same as or similar to the characteristic words in a preset characteristic word set or not; and under the condition that the words in the one or more words are the same as or similar to the characteristic words in the preset characteristic word set, determining the characteristic words in the preset characteristic word set, which are the same as or similar to the words in the one or more words, as the one or more characteristic words.
Another aspect of the present disclosure provides an object processing system including an obtaining module, a first determining module, a quantizing module, and a second determining module. The acquisition module is used for acquiring corpus information associated with the object; the first determining module is used for determining one or more characteristic words contained in the corpus information associated with the object, wherein each characteristic word corresponds to a characteristic word weight; the quantization module is configured to quantize the corpus information associated with the object based on a feature word weight corresponding to each feature word in the one or more feature words; and a second determining module for determining whether the object has a predetermined attribute according to a quantization result of the corpus information associated with the object.
According to the embodiment of the disclosure, the system further comprises a judging module and a third determining module. The judging module is used for judging whether the linguistic data information associated with the object contains one or more characteristic words before determining the one or more characteristic words contained in the linguistic data information associated with the object; and a third determining module, configured to determine that the object does not have the predetermined attribute when it is determined that the corpus information associated with the object does not include the one or more feature words.
According to an embodiment of the present disclosure, the second determining module includes a first judging unit and a first determining unit. The first judging unit is used for judging whether a quantization value corresponding to a quantization result of the corpus information associated with the object is greater than or equal to a preset threshold value or not; and a first determining unit configured to determine that the object has the predetermined attribute when it is determined that a quantization value corresponding to a quantization result of the corpus information associated with the object is greater than or equal to the preset threshold.
According to an embodiment of the present disclosure, the system further includes a fourth determining module, configured to determine a weight of each of the one or more feature words, where the fourth determining module includes an obtaining unit, a classifying unit, a second determining unit, a third determining unit, and a fourth determining unit. The acquiring unit is used for acquiring a training sample, wherein the training sample is used for determining the feature word weight of each feature word in the one or more feature words; the classification unit is used for classifying the training samples to obtain a first data set and a second data set, wherein the object corresponding to the first data set has the predetermined attribute, and the object corresponding to the second data set does not have the predetermined attribute; the second determining unit is used for determining the proportion of each feature word in the one or more feature words to all words contained in the first data set to obtain a first proportion; the third determining unit is used for determining the proportion of each feature word in the one or more feature words to all words contained in the second data set to obtain a second proportion; and a fourth determining unit, configured to determine a feature word weight of each feature word in the one or more feature words according to the first percentage and the second percentage.
According to an embodiment of the present disclosure, the first determining module includes a word segmentation unit, a second judging unit, and a fifth determining unit. The word segmentation unit is used for carrying out word segmentation processing on the corpus information associated with the object to obtain one or more words; the second judging unit is used for judging whether the words in the one or more words are the same as or similar to the characteristic words in the preset characteristic word set; and a fifth determining unit, configured to determine, as the one or more feature words, feature words in the preset feature word set that are the same as or similar to the words in the one or more words, when the words in the one or more words are the same as or similar to the feature words in the preset feature word set.
Another aspect of the disclosure provides a computer system comprising one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the object processing method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the object processing method as described above.
Another aspect of the present disclosure provides a computer program comprising computer executable instructions for implementing the object processing method as described above when executed.
According to the embodiment of the disclosure, the corpus information associated with the object can be quantized according to the feature word weight of the feature word contained in the corpus information associated with the object, and finally, whether the object has the technical means of the predetermined attribute can be judged according to the quantization result, so that the technical problem of low accuracy of identifying the new product in the related technology is at least partially overcome, and the technical effect of improving the accuracy of identifying the new product is further achieved.
Drawings
the above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
Fig. 1 schematically illustrates an exemplary system architecture to which an object processing method and an object processing system may be applied, according to an embodiment of the present disclosure;
FIG. 2 schematically shows a flow diagram of an object processing method according to an embodiment of the present disclosure;
FIG. 3A schematically illustrates a flow diagram of an object processing method according to another embodiment of the present disclosure;
FIG. 3B schematically shows a flow chart for determining whether an object has a predetermined property according to an embodiment of the present disclosure;
FIG. 3C schematically illustrates a flow diagram of an object processing method according to another embodiment of the present disclosure;
FIG. 3D schematically illustrates a flow chart for determining a weight for each of one or more feature words according to an embodiment of the present disclosure;
FIG. 3E schematically illustrates a flow chart for determining one or more feature words contained in corpus information associated with an object, in accordance with an embodiment of the present disclosure;
FIG. 4 schematically illustrates a block diagram of an object processing system according to an embodiment of the present disclosure;
FIG. 5A schematically illustrates a block diagram of an object processing system according to another embodiment of the present disclosure;
FIG. 5B schematically illustrates a block diagram of a second determination module according to an embodiment of the disclosure;
FIG. 5C schematically illustrates a block diagram of an object processing system according to another embodiment of the present disclosure;
FIG. 5D schematically illustrates a block diagram of a fourth determination module according to an embodiment of the disclosure;
FIG. 5E schematically illustrates a block diagram of a first determination module, in accordance with an embodiment of the present disclosure; and
Fig. 6 schematically shows a block diagram of a computer system suitable for implementing an object processing method according to an embodiment of the present disclosure.
Detailed Description
hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "a or B" should be understood to include the possibility of "a" or "B", or "a and B".
The embodiment of the disclosure provides an object processing method and an object processing system, wherein the object processing method comprises the steps of obtaining corpus information associated with an object; determining one or more characteristic words contained in the corpus information associated with the object, wherein each characteristic word corresponds to a characteristic word weight; quantifying corpus information associated with the object based on the feature word weight corresponding to each feature word in the one or more feature words; and determining whether the object has a predetermined attribute according to a quantization result of the corpus information associated with the object.
Fig. 1 schematically shows an exemplary system architecture to which an object processing method and an object processing system may be applied according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired and/or wireless communication links, and so forth.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as a shopping-like application, a web browser application, a search-like application, an instant messaging tool, a mailbox client, and/or social platform software, etc. (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
it should be noted that the object processing method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the object processing system provided by the embodiments of the present disclosure may be generally disposed in the server 105. The object processing method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the object processing system provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Alternatively, the object processing method provided by the embodiment of the present disclosure may also be executed by the terminal device 101, 102, or 103, or may also be executed by another terminal device different from the terminal device 101, 102, or 103. Accordingly, the object processing system provided by the embodiment of the present disclosure may also be disposed in the terminal device 101, 102, or 103, or in another terminal device different from the terminal device 101, 102, or 103.
For example, any one of the terminal devices 101, 102, or 103 (e.g., the terminal device 101, but not limited thereto) obtains corpus information associated with the object from a local, other terminal device, or a server. Then, the terminal device 101 may locally execute the object processing method provided by the embodiment of the present disclosure, or send the obtained corpus information to another terminal device, server, or server cluster, and execute the object processing method provided by the embodiment of the present disclosure by another terminal device, server, or server cluster that receives the corpus information.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 schematically shows a flow chart of an object handling method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S210 to S240, in which:
In operation S210, corpus information associated with an object is acquired.
According to the embodiment of the disclosure, corpus information associated with an object can be acquired from an object publishing platform. For example, the object publishing platform may include a wechat platform, a micro blog platform, or a company official website, and specifically, the corpus information describing the object, such as the product, may be crawled from one or more of these platforms through a web crawler technology, or may be obtained from corresponding servers of these platforms. For example, the company will market a latest notebook computer with the 9 th generation operating system in the new month, and the notebook nature is excellent and has a better price compared with other products.
In operation S220, one or more feature words included in corpus information associated with an object are determined, wherein each feature word corresponds to a feature word weight.
According to the embodiment of the disclosure, after the corpus information associated with the object is obtained, the feature words contained in the corpus information may be determined, and may be one or more feature words, and each feature word corresponds to a feature word weight. For example, the corpus information is the latest notebook computer with the 9 th generation operating system that the company will come into the market in the new month, and the notebook computer has excellent performance and is more favorable than other products in price. The feature words determined from the feature words can be newly listed and the latest notebook computer, wherein the weight of the feature words corresponding to the newly listed feature words can be 0.5, and the weight of the feature words corresponding to the latest notebook computer can be 0.8. It should be noted that the feature words may be determined after data analysis is performed on a large amount of corpus information on different platforms, or may be set manually, and the weight of each feature word may be determined according to the probability that the characterization object has the predetermined attribute.
In operation S230, corpus information associated with the object is quantized based on a feature word weight corresponding to each of the one or more feature words.
According to the embodiment of the disclosure, after the characteristic words are determined from the obtained corpus information, the corpus information associated with the object is quantized according to the characteristic word weight corresponding to each characteristic word. For example, after the feature word weight corresponding to each feature word is calculated according to a preset formula, a calculation result is obtained as a quantization value of the corpus information associated with the object, and specifically, for example, the feature word weights corresponding to each feature word may be added and summed, and the obtained summation result is used as the quantization value of the corpus information associated with the object.
In operation S240, it is determined whether the object has a predetermined attribute according to a quantization result of the corpus information associated with the object.
According to the embodiment of the disclosure, taking the object as an example of a product produced and designed by a brand manufacturer, determining whether the object has a predetermined attribute may be determining whether the product is a new product, and the brand manufacturer generally publishes information of the brand manufacturer on a platform such as an official microblog or an official website, wherein the information may include the product produced and designed by the brand manufacturer. How to effectively identify whether the information issued by the brand dealer contains the information of the new product or not can be quantified by the way, and then the quantified result is obtained, when the quantified value is higher, the information issued by the brand dealer can be determined to contain the information of the new product, and when the quantified value is lower, the information issued by the brand dealer can be determined not to contain the information of the new product. Specifically, the quantization result may be compared with a preset threshold, so that it may be determined whether the information issued by the brand owner includes information of a new product.
According to the embodiment of the disclosure, by taking the object as the brand name, the corpus information associated with the brand name can be acquired, and after the corpus information is quantized by adopting the method, whether the brand name issues the new product or not can be determined. Correspondingly, according to the quantization result of the corpus information associated with the object, whether the object has a predetermined attribute is determined to determine whether a brand dealer issues a new product. According to an embodiment of the present disclosure, with the above method of the present disclosure, it may also be determined whether any piece of corpus information is information about an object having a predetermined attribute. For example, the corpus information is quantized according to the feature word weight corresponding to the feature word contained in the acquired corpus information, so as to determine whether the corpus information is information describing an object having a predetermined attribute according to the quantization result. For example, it can be determined whether the corpus information is information for a new product from a piece of corpus information published on the corporate web.
According to the embodiment of the disclosure, the corpus information associated with the object can be quantized according to the feature word weight of the feature word contained in the corpus information associated with the object, and finally, whether the object has the technical means of the predetermined attribute can be judged according to the quantization result.
the method shown in fig. 2 is further described with reference to fig. 3A-3E in conjunction with specific embodiments.
fig. 3A schematically illustrates a flow chart of an object processing method according to another embodiment of the present disclosure.
as shown in fig. 3A, the object processing method further includes operations S250 to S260, in which:
in operation S250, before determining one or more feature words included in the corpus information associated with the object, it is determined whether the one or more feature words are included in the corpus information associated with the object.
In operation S260, in the case where it is determined that one or more feature words are not included in the corpus information associated with the object, it is determined that the object does not have a predetermined attribute.
According to an embodiment of the disclosure, after the corpus information associated with the object is obtained, it may be determined whether the corpus information associated with the object includes one or more feature words, and if it is determined that the corpus information associated with the object does not include one or more feature words, it is determined that the object does not have the predetermined attribute. If it is determined that the corpus information associated with the object includes one or more feature words, the method described in operations S220 to S240 is performed. For example, the corpus information is: the company will be in the bird nest opening year in the tomorrow. If the corpus information does not contain one or more feature words, the corpus information can be determined to be different according to the corpus of the new product. Or, the corpus information is: the 8 th generation notebook computer released by the company in the last year is reduced in price. If the corpus information does not contain one or more feature words, it can be determined that the corpus information is different from the corpus of the new product, and the notebook computer is not the new product.
Through the embodiment of the disclosure, under the condition that the corpus information has no characteristic words, the product is directly determined to be not a new product, so that the data processing efficiency is improved.
Fig. 3B schematically illustrates a flow chart for determining whether an object has a predetermined attribute according to an embodiment of the present disclosure.
As shown in fig. 3B, determining whether an object has a predetermined attribute according to a quantization result of corpus information associated with the object includes operations S241 to S242, in which:
in operation S241, it is determined whether a quantization value corresponding to a quantization result of corpus information associated with an object is greater than or equal to a preset threshold.
In operation S242, in a case where it is determined that a quantization value corresponding to a quantization result of corpus information associated with an object is greater than or equal to a preset threshold, the object is determined to have a predetermined attribute.
According to an embodiment of the present disclosure, determining whether the object has the predetermined attribute may be determining whether the corresponding quantization result is greater than or equal to a preset threshold, for example, weights corresponding to a plurality of feature words included in the corpus information are 0.5, 0.8, and 1.5, summing the weights of the feature words in the corpus information associated with the object according to a summation formula to obtain a score of the corpus information as 2.8, and if the preset threshold is 2.5, determining that the object has the predetermined attribute if the quantization value is greater than the preset threshold.
According to the embodiment of the disclosure, the size of the preset threshold can be artificially determined according to the actual situation, or the threshold meeting the actual recognition accuracy can be obtained after multiple times of verification is performed according to a large amount of training data.
through the embodiment of the disclosure, whether the product object has the preset attribute or not can be identified quickly and efficiently by comparing the quantization value corresponding to the quantization result of the corpus information with the preset threshold value. By adopting the method for setting the preset threshold, the size of the preset threshold can be adjusted according to the actual situation, so that the method is more in line with the actual situation.
Fig. 3C schematically shows a flow chart of an object processing method according to another embodiment of the present disclosure.
it should be noted that, in this embodiment, in addition to the operations S210 to S240 described in fig. 2, an operation S270 is further included, for the sake of brevity of description, the operations S210 to S240 are not described herein again, and specifically refer to the corresponding description of fig. 2, as shown in fig. 3C, the object processing method further includes an operation S270, where:
In operation S270, a weight of each of the one or more feature words is determined.
According to the embodiment of the disclosure, the weights of different feature words may be the same or different, and the weights of the feature words may be used for characterizing whether the object has the degree of the predetermined attribute.
Fig. 3D schematically illustrates a flow chart for determining a weight for each of one or more feature words according to an embodiment of the present disclosure.
As shown in fig. 3D, determining the weight of each of the one or more feature words includes operations S271 to S275, where:
In operation S271, a training sample is obtained, where the training sample is used to determine a feature word weight of each of one or more feature words.
According to the embodiment of the disclosure, the training sample includes a plurality of pieces of corpus information, and the sample source may be corpus information obtained from different platforms or corpus information on the same platform.
In operation S272, the training samples are classified to obtain a first data set and a second data set, where the object corresponding to the first data set has a predetermined attribute, and the object corresponding to the second data set does not have the predetermined attribute.
According to the embodiment of the disclosure, the obtained training samples are classified, and whether each piece of corpus information in the training samples is corpus information describing an object with a predetermined attribute can be marked, so that a first data set describing the object with the predetermined attribute and a second data set not describing the object with the predetermined attribute can be obtained. Specifically, taking whether the corpus information is used for describing a new product as an example, the corpus information in the training sample is marked as a first data set used for describing the new product and a second data set not used for describing the new product, wherein an object corresponding to the first data set has a predetermined attribute indicating that the object described by the corpus information in the first data set is the new product, and an object corresponding to the second data set does not have the predetermined attribute indicating that the object described by the corpus information in the first data set is not the new product.
In operation S273, a ratio of each of the one or more feature words to all words included in the first data set is determined, and a first ratio is obtained.
According to the embodiment of the disclosure, a word segmentation technology may be adopted to perform word segmentation on the corpus information included in the first data set, count the total number of words included in the first data set, count the total number of occurrences of each feature word in the corpus information included in the first data set, and determine the proportion of each feature word in the one or more feature words in all words included in the first data set according to the proportion of the total number of occurrences of each feature word in the corpus information included in the first data set to the total number of words included in the first data set.
In operation S274, a ratio of each of the one or more feature words to all words included in the second data set is determined, resulting in a second ratio.
According to the embodiment of the disclosure, a word segmentation technology may be adopted to perform word segmentation on the corpus information included in the second data set, count the total number of words included in the second data set, count the total number of occurrences of each feature word in the corpus information included in the second data set, and determine the proportion of each feature word in the one or more feature words in all words included in the second data set according to the proportion of the total number of occurrences of each feature word in the corpus information included in the second data set to the total number of words included in the second data set.
in operation S275, a feature word weight of each of the one or more feature words is determined according to the first and second ratios.
according to an embodiment of the present disclosure, determining the feature word weight of each of the one or more feature words may use the following formula:
in general, the words contained in the second data set are very large, so that Neg w is very large, therefore, the second occupation ratio is scaled to a reasonable range by adopting a logarithmic function, so that the calculation precision can be improved, and the accuracy of the recognition object with the predetermined attribute is further improved.
By the embodiment of the disclosure, the characteristic word weight of each characteristic word is determined by adopting the above mode, the object can be effectively represented to have the preset attribute, and the accuracy of identifying the object to have the preset attribute is improved.
Fig. 3E schematically illustrates a flow chart for determining one or more feature words contained in corpus information associated with an object according to an embodiment of the present disclosure.
As shown in fig. 3E, determining one or more feature words included in corpus information associated with an object includes operations S221 to S223, in which:
In operation S221, the corpus information associated with the object is word-segmented to obtain one or more words.
According to an embodiment of the present disclosure, for example, the corpus information associated with the object is "the operating system that the company will be listed in the new place of this month is the first notebook computer of the 9 th generation", and the corpus information associated with the object may be subjected to word segmentation processing to obtain the following words: the company, future, month, new market, first money, operating system, yes, 9 th generation, first money, notebook computer.
In operation S222, it is determined whether a term of the one or more terms is the same as or similar to a feature term of a preset feature term set.
According to the embodiment of the disclosure, the preset feature word set can be set by adding manually after big data analysis, and the number in the preset feature word set can be set according to actual recognition conditions. According to an embodiment of the present disclosure, the preset feature words in the feature word set may include: new, latest, original, novel and first-time marketing. It should be noted that the feature words in the preset feature word set are not limited to the above listed cases, and are not described herein again.
According to the embodiment of the disclosure, one or more words obtained by word segmentation can be compared with the feature words in the preset feature word set to judge whether the same or similar words exist, for example, the corpus information contains a new listing which is the same as the new listing in the feature words in the preset feature word set; the corpus information includes a first part similar to the latest part of the feature words in the preset feature word set.
In operation S223, in a case that a word in the one or more words is the same as or similar to a feature word in the preset feature word set, a feature word in the preset feature word set that is the same as or similar to a word in the one or more words is determined as the one or more feature words.
by the embodiment of the disclosure, one or more words obtained by word segmentation are compared with the feature words in the preset feature word set, so that the method for determining the feature words in the corpus information is provided, whether the corpus information contains the feature words or not can be rapidly determined, and the object processing efficiency is improved.
Fig. 4 schematically shows a block diagram of an object processing system according to an embodiment of the present disclosure.
as shown in fig. 4, the object processing system 400 includes an acquisition module 410, a first determination module 420, a quantization module 430, and a second determination module 440.
The obtaining module 410 is configured to obtain corpus information associated with an object.
The first determining module 420 is configured to determine one or more feature words included in the corpus information associated with the object, where each feature word corresponds to a feature word weight.
the quantization module 430 is configured to quantize the corpus information associated with the object based on a feature word weight corresponding to each feature word in the one or more feature words.
the second determining module 440 is configured to determine whether the object has a predetermined attribute according to a quantization result of the corpus information associated with the object.
According to the embodiment of the disclosure, the corpus information associated with the object can be quantized according to the feature word weight of the feature word contained in the corpus information associated with the object, and finally, whether the object has the technical means of the predetermined attribute can be judged according to the quantization result.
Fig. 5A schematically illustrates a block diagram of an object processing system according to another embodiment of the present disclosure.
As shown in fig. 5A, the object processing system 400 includes a judgment module 450 and a third determination module 460 in addition to the acquisition module 410, the first determination module 420, the quantization module 430 and the second determination module 440. Wherein:
The determining module 450 is configured to determine whether the corpus information associated with the object includes one or more feature words before determining the one or more feature words included in the corpus information associated with the object.
The third determining module 460 is configured to determine that the object does not have the predetermined attribute if the corpus information associated with the object does not include one or more feature words.
Through the embodiment of the disclosure, under the condition that the corpus information has no characteristic words, the product is directly determined to be not a new product, so that the data processing efficiency is improved.
fig. 5B schematically illustrates a block diagram of a second determination module according to an embodiment of the disclosure.
as shown in fig. 5B, according to an embodiment of the present disclosure, the second determining module 440 includes a first judging unit 441 and a first determining unit 442.
The first determining unit 441 is configured to determine whether a quantization value corresponding to a quantization result of the corpus information associated with the object is greater than or equal to a preset threshold.
The first determination unit 442 is configured to determine that the object has the predetermined attribute if it is determined that the quantization value corresponding to the quantization result of the corpus information associated with the object is greater than or equal to a preset threshold.
Through the embodiment of the disclosure, whether the product object has the preset attribute or not can be identified quickly and efficiently by comparing the quantization value corresponding to the quantization result of the corpus information with the preset threshold value. By adopting the method for setting the preset threshold, the size of the preset threshold can be adjusted according to the actual situation, so that the method is more in line with the actual situation.
Fig. 5C schematically illustrates a block diagram of an object processing system according to another embodiment of the present disclosure.
as shown in fig. 5C, the object processing system 400 includes an acquisition module 410, a first determination module 420, a quantization module 430, and a second determination module 440 in addition. According to an embodiment of the present disclosure, the object processing system 400 further includes a fourth determining module 470 for determining a weight of each of the one or more feature words.
Fig. 5D schematically illustrates a block diagram of a fourth determination module according to an embodiment of the disclosure.
as shown in fig. 5D, the fourth determination module 470 includes an acquisition unit 471, a classification unit 472, a second determination unit 473, a third determination unit 474, and a fourth determination unit 475.
The obtaining unit 471 is configured to obtain a training sample, where the training sample is used to determine a feature word weight of each of one or more feature words.
The classification unit 472 is configured to classify the training samples to obtain a first data set and a second data set, where an object corresponding to the first data set has a predetermined attribute, and an object corresponding to the second data set does not have the predetermined attribute.
The second determining unit 473 is configured to determine a ratio of each of the one or more feature words to all words included in the first data set, so as to obtain a first ratio.
The third determining unit 474 is configured to determine a ratio of each of the one or more feature words to all words included in the second data set, so as to obtain a second ratio.
the fourth determining unit 475 is configured to determine a feature word weight of each feature word of the one or more feature words according to the first percentage and the second percentage.
by the embodiment of the disclosure, the characteristic word weight of each characteristic word is determined by adopting the above mode, the object can be effectively represented to have the preset attribute, and the accuracy of identifying the object to have the preset attribute is improved.
Fig. 5E schematically illustrates a block diagram of a first determination module according to an embodiment of the disclosure.
As shown in fig. 5E, according to an embodiment of the present disclosure, the first determining module 420 includes a word segmentation unit 421, a second judgment unit 422, and a fifth determination unit 423.
The word segmentation unit 421 is configured to perform word segmentation processing on the corpus information associated with the object to obtain one or more words.
the second determining unit 422 is configured to determine whether a term in the one or more terms is the same as or similar to a feature term in the preset feature term set.
The fifth determining unit 423 is configured to determine, as the one or more feature words, feature words in the preset feature word set that are the same as or similar to the words in the one or more words, when the words in the one or more words are the same as or similar to the feature words in the preset feature word set.
By the embodiment of the disclosure, one or more words obtained by word segmentation are compared with the feature words in the preset feature word set, so that the method for determining the feature words in the corpus information is provided, whether the corpus information contains the feature words or not can be rapidly determined, and the object processing efficiency is improved.
It is understood that the obtaining module 410, the first determining module 420, the quantifying module 430, the second determining module 440, the judging module 450, the third determining module 460 and the fourth determining module 470 may be combined into one module to be implemented, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present invention, at least one of the obtaining module 410, the first determining module 420, the quantifying module 430, the second determining module 440, the judging module 450, the third determining module 460, and the fourth determining module 470 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in a suitable combination of three implementations of software, hardware, and firmware. Alternatively, at least one of the obtaining module 410, the first determining module 420, the quantifying module 430, the second determining module 440, the judging module 450, the third determining module 460 and the fourth determining module 470 may be at least partially implemented as a computer program module, which when executed by a computer, may perform the functions of the respective modules.
it should be noted that, the object processing system part in the embodiment of the present disclosure corresponds to the object processing method part in the embodiment of the present disclosure, and the description of the object processing system part specifically refers to the object processing method part, which is not described herein again.
Fig. 6 schematically shows a block diagram of a computer system suitable for implementing an object processing method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 6 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
as shown in fig. 6, a computer system 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. Processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 601 may also include onboard memory for caching purposes. Processor 601 may include a single processing unit or multiple processing units for performing the different actions of the method flows described with reference to fig. 2, 3A-3E in accordance with embodiments of the present disclosure.
in the RAM 603, various programs and data necessary for the operation of the system 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations described above with reference to fig. 2, 3A to 3E by executing programs in the ROM 602 and/or the RAM 603. It is to be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform the various operations described above with reference to fig. 2, 3A-3E by executing programs stored in the one or more memories.
according to an embodiment of the present disclosure, system 600 may also include an input/output (I/O) interface 605, input/output (I/O) interface 605 also connected to bus 604. The system 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. 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 needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
According to an embodiment of the present disclosure, the method described above with reference to the flow chart may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
it should be noted that the computer readable storage medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing. According to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 602 and/or RAM 603 described above and/or one or more memories other than the ROM 602 and RAM 603.
the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As another aspect, the present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer-readable storage medium carries one or more programs which, when executed by a device, cause the device to perform an object processing method comprising obtaining corpus information associated with an object; determining one or more characteristic words contained in the corpus information associated with the object, wherein each characteristic word corresponds to a characteristic word weight; quantifying corpus information associated with the object based on the feature word weight corresponding to each feature word in the one or more feature words; and determining whether the object has a predetermined attribute according to a quantization result of the corpus information associated with the object. Optionally, the object processing method further includes, before determining one or more feature words included in the corpus information associated with the object, determining whether the corpus information associated with the object includes one or more feature words; and determining that the object does not have the predetermined attribute in the case where it is determined that the corpus information associated with the object does not include the one or more feature words. Optionally, determining whether the object has the predetermined attribute according to a quantization result of the corpus information associated with the object includes: judging whether a quantization value corresponding to a quantization result of the corpus information associated with the object is greater than or equal to a preset threshold value; and determining that the object has the predetermined attribute under the condition that the quantization value corresponding to the quantization result of the corpus information associated with the object is judged to be greater than or equal to the preset threshold value. Optionally, the object processing method further includes determining a weight of each of the one or more feature words, including: acquiring a training sample, wherein the training sample is used for determining the feature word weight of each feature word in one or more feature words; classifying the training samples to obtain a first data set and a second data set, wherein objects corresponding to the first data set have preset attributes, and objects corresponding to the second data set do not have preset attributes; determining the proportion of each feature word in the one or more feature words to all words contained in the first data set to obtain a first proportion; determining the proportion of each feature word in the one or more feature words to all words contained in the second data set to obtain a second proportion; and determining the characteristic word weight of each characteristic word in the one or more characteristic words according to the first proportion and the second proportion. Optionally, determining one or more feature words included in the corpus information associated with the object includes: performing word segmentation processing on the corpus information associated with the object to obtain one or more words; judging whether the words in the one or more words are the same as or similar to the characteristic words in the preset characteristic word set or not; and determining the characteristic words in the preset characteristic word set, which are the same as or similar to the words in the one or more words, as the one or more characteristic words under the condition that the words in the one or more words are the same as or similar to the characteristic words in the preset characteristic word set.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (12)
1. An object processing method, comprising:
obtaining corpus information associated with an object;
Determining one or more characteristic words contained in the corpus information associated with the object, wherein each characteristic word corresponds to a characteristic word weight;
Quantifying the corpus information associated with the object based on a feature word weight corresponding to each feature word in the one or more feature words; and
and determining whether the object has a preset attribute according to a quantization result of the corpus information associated with the object.
2. the method of claim 1, wherein the method further comprises:
Before determining one or more characteristic words contained in the corpus information associated with the object, judging whether the corpus information associated with the object contains the one or more characteristic words; and
determining that the object does not have the predetermined attribute when it is determined that the corpus information associated with the object does not include the one or more feature words.
3. The method according to claim 1, wherein determining whether the object has a predetermined attribute according to a quantization result of the corpus information associated with the object comprises:
Judging whether a quantization value corresponding to a quantization result of the corpus information associated with the object is greater than or equal to a preset threshold value; and
and determining that the object has the predetermined attribute when the quantization value corresponding to the quantization result of the corpus information associated with the object is greater than or equal to the preset threshold.
4. The method of claim 1, wherein the method further comprises:
Determining a weight of each of the one or more feature words, including:
Obtaining a training sample, wherein the training sample is used for determining the feature word weight of each feature word in the one or more feature words;
Classifying the training samples to obtain a first data set and a second data set, wherein the object corresponding to the first data set has the predetermined attribute, and the object corresponding to the second data set does not have the predetermined attribute;
Determining the proportion of each feature word in the one or more feature words to all words contained in the first data set to obtain a first proportion;
Determining the proportion of each feature word in the one or more feature words to all words contained in the second data set to obtain a second proportion; and
And determining the characteristic word weight of each characteristic word in the one or more characteristic words according to the first proportion and the second proportion.
5. The method of claim 1, wherein determining one or more feature words contained in the corpus information associated with the object comprises:
Performing word segmentation processing on the corpus information associated with the object to obtain one or more words;
judging whether the words in the one or more words are the same as or similar to the characteristic words in a preset characteristic word set or not; and
Determining the characteristic words in the preset characteristic word set, which are the same as or similar to the words in the one or more words, as the one or more characteristic words when the words in the one or more words are the same as or similar to the characteristic words in the preset characteristic word set.
6. An object handling system comprising:
The acquisition module is used for acquiring corpus information associated with the object;
a first determining module, configured to determine one or more feature words included in the corpus information associated with the object, where each feature word corresponds to a feature word weight;
A quantization module, configured to quantize the corpus information associated with the object based on a feature word weight corresponding to each feature word in the one or more feature words; and
And the second determining module is used for determining whether the object has the preset attribute according to the quantization result of the corpus information associated with the object.
7. The system of claim 6, wherein the system further comprises:
A determining module, configured to determine whether the corpus information associated with the object includes one or more feature words before determining the one or more feature words included in the corpus information associated with the object; and
A third determining module, configured to determine that the object does not have the predetermined attribute when it is determined that the corpus information associated with the object does not include the one or more feature words.
8. the system of claim 6, wherein the second determination module comprises:
A first judging unit, configured to judge whether a quantization value corresponding to a quantization result of the corpus information associated with the object is greater than or equal to a preset threshold; and
A first determining unit, configured to determine that the object has the predetermined attribute when it is determined that a quantization value corresponding to a quantization result of the corpus information associated with the object is greater than or equal to the preset threshold.
9. the system of claim 6, wherein the system further comprises:
A fourth determining module, configured to determine a weight of each of the one or more feature words, where the fourth determining module includes:
the training device comprises an acquisition unit, a calculation unit and a display unit, wherein the acquisition unit is used for acquiring a training sample, and the training sample is used for determining the feature word weight of each feature word in one or more feature words;
The classification unit is used for classifying the training samples to obtain a first data set and a second data set, wherein the object corresponding to the first data set has the predetermined attribute, and the object corresponding to the second data set does not have the predetermined attribute;
A second determining unit, configured to determine a ratio of each feature word in the one or more feature words to all words included in the first data set, so as to obtain a first ratio;
A third determining unit, configured to determine a ratio of each feature word in the one or more feature words to all words included in the second data set, so as to obtain a second ratio; and
A fourth determining unit, configured to determine a feature word weight of each feature word in the one or more feature words according to the first percentage and the second percentage.
10. The system of claim 6, wherein the first determination module comprises:
The word segmentation unit is used for carrying out word segmentation processing on the corpus information associated with the object to obtain one or more words;
The second judging unit is used for judging whether the words in the one or more words are the same as or similar to the characteristic words in a preset characteristic word set or not; and
a fifth determining unit, configured to determine, as the one or more feature words, feature words in the preset feature word set that are the same as or similar to the words in the one or more words when the words in the one or more words are the same as or similar to the feature words in the preset feature word set.
11. A computer system, comprising:
One or more processors;
A memory for storing one or more programs,
Wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the object processing method of any of claims 1 to 5.
12. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the object processing method of any one of claims 1 to 5.
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