CN107908792B - Information push method and device - Google Patents
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Abstract
本申请实施例公开了信息推送方法和装置。该方法的一具体实施方式包括：接收包含待检测文本和该待检测文本的题材的文本检测请求；确定该待检测文本的目标词和目标句，并基于该目标词在该待检测文本中的位置和该目标句在该待检测文本中的位置，生成该待检测文本的词句分布信息；提取与该待检测文本的类型相匹配的结构检测模型，将该词句分布信息输入至该结构检测模型，得到该待检测文本的结构评价信息；推送包含该待检测文本的结构评价信息的文本检测结果。该实施方式提高了对文本的检测的灵活性。
The embodiments of the present application disclose an information push method and device. A specific implementation of the method includes: receiving a text detection request including the text to be detected and the subject matter of the text to be detected; determining the target word and target sentence of the text to be detected, and based on the target word in the text to be detected. The position and the position of the target sentence in the text to be detected generate word and sentence distribution information of the text to be detected; extract a structure detection model that matches the type of the text to be detected, and input the word and sentence distribution information into the structure detection model , obtain the structure evaluation information of the text to be detected; push the text detection result including the structure evaluation information of the text to be detected. This embodiment improves the flexibility of detection of text.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to the technical field of internet, and particularly relates to an information pushing method and device.
Background
With the popularization and spread of Chinese, Chinese writing is more and more important. After writing in chinese, the written article usually needs to be checked and reviewed to ensure that the written content is error-free and readable.
The existing method for correcting articles is usually manual correction, or correction is performed by using the existing auxiliary products in the field of Chinese writing, and these products usually only list various writing materials for selection by users, and cannot perform processing such as review, correction and the like on the text to be detected. The existing English text detection tool is not suitable for Chinese detection.
Disclosure of Invention
The embodiment of the application provides an information pushing method and device.
In a first aspect, an embodiment of the present application provides an information pushing method, where the method includes: receiving a text detection request containing a text to be detected and a subject of the text to be detected; determining target words and target sentences of the text to be detected, and generating word and sentence distribution information of the text to be detected based on the position of the target words in the text to be detected and the position of the target sentences in the text to be detected, wherein the target sentences are sentences of which the sentence patterns are matched with any preset sentence pattern in a preset sentence pattern set; extracting a structure detection model matched with the subject, inputting the word and sentence distribution information into the structure detection model to obtain structure evaluation information of the text to be detected, wherein the structure detection model is used for representing the corresponding relation between the word and sentence distribution information and the structure evaluation information; and pushing a text detection result containing the structure evaluation information of the text to be detected.
In some embodiments, the text detection result further includes a sentence pattern detection result, and the sentence pattern detection result is generated by: segmenting the text to be detected sentence by sentence; for each sentence after segmentation, determining whether the sentence has wrongly matched words, if so, extracting recommended words matched with the words, and generating prompt information for prompting that the sentence has matching problems, wherein the prompt information comprises the recommended words; summarizing the generated prompt information to generate sentence pattern detection results.
In some embodiments, before summarizing the generated hint information and generating the sentence detection result, the step of generating the sentence detection result further comprises: and for each target sentence, taking the preset sentence pattern matched with the target sentence as a target preset sentence pattern, optimizing the target sentence based on a preset optimization rule matched with the target preset sentence pattern, and generating prompt information for prompting the optimization of the sentence.
In some embodiments, the text detection results further include word detection results, the word detection results generated by: extracting key words and poems in the text to be detected; for each extracted keyword, in response to determining that the keyword is a noun, searching matching information corresponding to the keyword from a preset keyword matching information table, in response to determining that the keyword is an adjective, searching a synonym of the keyword from a preset synonym table, and generating word recommendation information containing the matching information or the synonym; for each extracted poetry sentence, searching a related poetry sentence related to the poetry sentence from a preset poetry sentence library to generate poetry sentence recommendation information containing the related poetry sentence; and summarizing the generated word recommendation information and the poetry recommendation information to generate a word detection result.
In some embodiments, the text detection results further comprise a wrongly written word detection result, the wrongly written word detection result being generated by: extracting a poetry sentence in a text to be detected; segmenting words of a text to be detected except for poetry, matching each word after segmentation with a preset word library, and matching the poetry with the preset poetry library; and determining the words containing the wrongly written characters in the text to be detected and the poetry sentences containing the wrongly written characters in the text to be detected based on the matching result so as to generate a wrongly written character detection result containing wrongly written character information.
In a second aspect, an embodiment of the present application provides an information pushing apparatus, where the apparatus includes: the receiving unit is configured to receive a text detection request containing a text to be detected and a subject of the text to be detected; the generating unit is configured to determine a target word and a target sentence of the text to be detected, and generate word and sentence distribution information of the text to be detected based on the position of the target word in the text to be detected and the position of the target sentence in the text to be detected, wherein the target sentence is a sentence with a sentence pattern matched with any preset sentence pattern in a preset sentence pattern set; the input unit is configured to extract a structure detection model matched with the subject, and input the word and sentence distribution information into the structure detection model to obtain structure evaluation information of the text to be detected, wherein the structure detection model is used for representing the corresponding relation between the word and sentence distribution information and the structure evaluation information; and the pushing unit is configured for pushing a text detection result containing the structure evaluation information of the text to be detected.
In some embodiments, the text detection result further comprises a sentence detection result, and the apparatus further comprises: the segmentation unit is configured to segment the text to be detected sentence by sentence; the first determining unit is configured to determine whether a wrongly matched word exists in each sentence after segmentation, if so, extract a recommended word matched with the word, and generate prompt information for prompting that the sentence has a matching problem, wherein the prompt information comprises the recommended word; and the first summarizing unit is configured to summarize the generated prompt information and generate a sentence pattern detection result.
In some embodiments, the apparatus further comprises: and the optimization unit is configured to, for each target sentence, use a preset sentence pattern matched with the target sentence as a target preset sentence pattern, optimize the target sentence based on a preset optimization rule matched with the target preset sentence pattern, and generate prompt information for prompting optimization of the sentence.
In some embodiments, the text detection results further include word detection results, the apparatus further comprising: the first extraction unit is configured to extract keywords and verses in the text to be detected; the first searching unit is configured to search matching information corresponding to each extracted keyword from a preset keyword matching information table in response to the fact that the keyword is determined to be a noun, search synonyms of the keyword from a preset synonym table in response to the fact that the keyword is determined to be an adjective, and generate word recommendation information containing the matching information or the synonyms; the second searching unit is configured to search a poetry sentence correlated with each extracted poetry sentence from a preset poetry sentence library to generate poetry sentence recommendation information containing the correlated poetry sentences; and the second summarizing unit is configured to summarize the generated word recommendation information and poetry recommendation information to generate a word detection result.
In some embodiments, the text detection results further include mispronounced word detection results, the apparatus further comprising: the second extraction unit is configured to extract the verses in the text to be detected; the matching unit is configured for segmenting the text to be detected except the poetry sentence, matching each segmented word with a preset word library, and matching the poetry sentence with the preset poetry sentence library; and the second determining unit is configured to determine words containing wrongly written characters in the text to be detected and poems containing wrongly written characters in the text to be detected based on the matching result so as to generate a wrongly written character detection result containing wrongly written character information.
In a third aspect, an embodiment of the present application provides a server, including: one or more processors; a storage device for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement a method as in any embodiment of the information push method.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method as in any embodiment of an information pushing method.
According to the information pushing method and device provided by the embodiment of the application, the target words and the target sentences of the received text to be detected are determined, so that word and sentence distribution information is generated at the positions based on the target words and the target sentences, then the word and sentence distribution information is input into the structure detection model matched with the subject of the text to be detected, the structure evaluation information of the text to be detected is obtained, and finally the text detection result containing the structure evaluation information is pushed, so that the text does not need to be reviewed manually, and the flexibility of text detection is improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of an information push method according to the present application;
FIG. 3 is a schematic diagram of an application scenario of an information push method according to the present application;
FIG. 4 is a schematic block diagram of one embodiment of an information pushing device according to the present application;
FIG. 5 is a block diagram of a computer system suitable for use in implementing a server according to embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which the information push method or the information push apparatus of the present application can be applied.
As shown in fig. 1, the system architecture 100 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, wireless communication links, or fiber optic cables, to name a few.
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 various communication client applications installed thereon, such as a text editing application, an image recognition application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
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 that provides various services, for example, a cloud server that provides a review service for information such as text to be detected transmitted by the terminal apparatuses 101, 102, 103. The cloud server may analyze and perform other processing on the received data such as the text detection request, and feed back a processing result (e.g., a text detection result) to the terminal device.
It should be noted that the information pushing method provided in the embodiment of the present application is generally executed by the server 105, and accordingly, the information pushing apparatus is generally disposed in the server 105.
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.
With continued reference to FIG. 2, a flow 200 of one embodiment of an information push method according to the present application is shown. The information pushing method comprises the following steps:
In this embodiment, an electronic device (for example, the server 105 shown in fig. 1) on which the information push method operates may receive, through a wired connection manner or a wireless connection manner, a text detection request that is sent by a client (for example, the terminal devices 101, 102, and 103 shown in fig. 1) and includes a text to be detected and a subject of the text to be detected. The text to be detected can be a Chinese article with various subject matters, such as narrative, discussion paper, narrative text, prose, poem, application letter, recommendation letter and the like. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
It should be noted that the text verification request may further include other information, such as a tag of the text to be verified. Here, the tag may be manually input by the user, e.g. "friendship, friend".
In practice, a text detection application or a browser application may be installed in a terminal device (such as terminal devices 101, 102, and 103 shown in fig. 1) used by a user, and the user may edit the text to be detected in a text detection page of the text detection application or the browser application and select a subject of the text to be detected. Then, the user may send a text detection request including the text to be detected and the subject to the electronic device by clicking a "start detection" button in a page of the text detection type application or the browser application. In addition, the text detection page of the text detection application or the browser application may also support image importing, and in this case, characters in the image may be recognized by using an OCR (Optical Character Recognition) technology, and the recognized characters may be determined as characters to be verified.
In this embodiment, the electronic device may first determine the target words and the target sentences of the text to be detected. It should be noted that the target sentence may be a sentence having a sentence pattern matching any one of a set of preset sentence patterns, wherein the preset sentence patterns may include, but are not limited to, a proportional sentence, a anthropomorphic sentence, a question-making sentence, a dual sentence, and the like. It should be noted that the target word may include one or more nouns (which may be referred to as a central word) with a higher frequency of occurrence in a title, a head (e.g., a first natural segment, a first two natural segments, etc.), and a tail (e.g., a last natural segment, a last three natural segments, etc.) of the text to be detected; synonyms and synonyms of the central words in the text to be detected can also be included; the method can also comprise modifiers used for modifying the central word, the synonym and the synonym in the text to be detected. As an example, if a noun with a high frequency of occurrence in the title, head, and tail of a certain text is "friendship" (i.e., a head word), the target word may include the following words taken from the text: "friendship", "friend", "girlfriend", "know-your-own", "brother", "close without room", "liver and gall photo", "better and better flower".
After determining the target words and the target sentences of the text to be detected, the electronic device may determine the positions of the target words and the target sentences in the text to be detected, and generate word and sentence distribution information of the text to be detected based on the positions of the target words and the target sentences in the text to be detected. The sentence distribution information may include a target word type identifier (for example, a character string composed of various characters to distinguish the types of target words) indicating the type of each target word (for example, a central word, a synonym, a modifier, and the like), a target sentence type identifier (for example, a character string composed of various characters to distinguish the types of target sentences) indicating the type of each target sentence (for example, a character string composed of various characters to distinguish the types of target sentences), information (for example, a paragraph number) indicating the position of each target word in the text to be detected, information indicating the position of each target word in the text to be detected, and the like.
And 203, extracting a structure detection model matched with the subject, and inputting the word and sentence distribution information into the structure detection model to obtain structure evaluation information of the text to be detected.
In this embodiment, the electronic device may be pre-stored with a structure detection model matching with texts of various subjects. After generating the word and sentence distribution information of the text to be detected, the electronic device may extract a structure detection model matched with the subject of the text to be detected, and input the generated word and sentence distribution information to the structure detection model to obtain structure evaluation information of the text to be detected. The structure evaluation information may be a score (e.g., 80, 90, 100, etc.) or a rating (e.g., a level a, a level B, a level C, etc.) for representing the goodness of the structure layout of the text to be detected. The structure detection model may be used to represent the correspondence between the word and sentence distribution information and the structure evaluation information, and for example, the structure detection model may be a correspondence table between the word and sentence distribution information and the structure evaluation information, which is prepared by a technician based on statistics of the word and sentence distribution information and the structure evaluation information of a large number of texts.
In some optional implementations of this embodiment, the structure detection model may be generated by: first, a preset training sample may be extracted, where the training sample may include word and sentence distribution information and structure evaluation information of each of a plurality of texts. The electronic device may use a machine learning method to obtain the structure detection model by training using a conventional classification model (e.g., a decision tree, a neural network, a logistic regression model, etc.) with the word and sentence distribution information in the training sample as an input and the structure evaluation information in the training sample as an output. It should be noted that the above method for training the model is a well-known technique widely studied and applied at present, and is not described herein again.
And step 204, pushing a text detection result containing the structure evaluation information of the text to be detected.
In this embodiment, the electronic device may push a text detection result including structure evaluation information of the text to be detected to a client that sends the text detection request. Here, the text detection result may further include other information, such as the determined central word, and layout description information for describing the structural part of the text to be detected.
In some optional implementations of this embodiment, the text detection result may further include a sentence detection result. The sentence pattern detection result can be generated through the following steps: in the first step, the text to be detected can be segmented sentence by sentence. And secondly, determining whether the sentence has wrongly matched words or not for each segmented sentence, if so, extracting recommended words matched with the words, and generating prompt information for prompting that the sentence has matching problems, wherein the prompt information can comprise the recommended words. Specifically, for each sentence after segmentation, the electronic device may first determine a subject, a predicate, an object, a predicate, a shape, and a complement in the sentence; then, whether the sentence has a word with a wrong collocation can be determined by determining whether the subject is collocated with the predicate (or verb), whether the predicate is collocated with the subject, whether the object is collocated with the predicate, and the like; if the word with the wrong collocation is determined to exist, a preset word collocated with the word can be extracted, the extracted word is used as a recommended word, and prompt information including the recommended word is generated. And thirdly, summarizing the generated prompt information to generate a sentence pattern detection result. As an example, the text to be detected includes a character string "make people feel happy" and "make happy" is a verb, and the subject is generally "flower fragrance", "fragrance", and the like, rather than a person, and therefore, it is possible to determine "person" as a word matching a mistake, and determine the words "flower fragrance", "fragrance", and the like as a recommended word.
In some optional implementation manners of this embodiment, before summarizing the generated prompt information and generating the sentence detection result, the electronic device may further perform the following steps: and for each target sentence, taking the preset sentence pattern matched with the target sentence as a target preset sentence pattern, optimizing the target sentence based on a preset optimization rule matched with the target preset sentence pattern, and generating prompt information for prompting the optimization of the sentence. As an example, the target preset sentence pattern is a proportional sentence, the proportional sentence usually includes at least three structurally similar phrases, and verbs or adverbs in the three phrases are the same, e.g., "XX, like a, like B, more like C". The optimization rule matching the collation sentence may be: determining whether an adjective exists before a noun in each short sentence forming the ranking sentence, and if not, adding the adjective for adjecting the noun before the noun.
In some optional implementations of the present embodiment, the text detection result may further include a word detection result. The word detection result can be generated through the following steps: in the first step, keywords (e.g., words matched with nouns and adjectives in a preset keyword lexicon) and poems in the text to be detected can be extracted. And secondly, for each extracted keyword, in response to determining that the keyword is a noun, searching matching information (such as an adjective for modifying the keyword) corresponding to the keyword from a preset keyword matching information table, in response to determining that the keyword is an adjective, searching a synonym of the keyword from a preset synonym table, and generating word recommendation information containing the matching information or the synonym. And thirdly, for each extracted poetry sentence, searching a preset poetry sentence library for a related poetry sentence (for example, poetry sentences representing the same theme (for example, friendship, country of thinking and the like)) associated with the poetry sentence, and generating poetry sentence recommendation information containing the related poetry sentences. And fourthly, summarizing the generated word recommendation information and the poetry recommendation information to generate a word detection result.
In some optional implementation manners of this embodiment, the text detection result may further include a wrongly written word detection result. The detection result of the wrongly written words can be generated by the following steps: firstly, extracting the verses in the text to be detected. And secondly, segmenting the text to be detected except the poetry sentence, matching each segmented word with a preset word library by using a character string matching mode, and matching the poetry sentence with the preset poetry sentence library. And thirdly, determining words containing wrongly written characters in the text to be detected and verses containing wrongly written characters in the text to be detected based on the matching result so as to generate a wrongly written character detection result containing wrongly written character information. It should be noted that the electronic device may also perform the detection of the wrongly written characters by using other detection manners of the wrongly written characters, for example, by performing character string matching on words or phrases and a preset lexicon or a library of wrongly written characters, to determine the words containing the wrongly written characters in the text to be detected and the verses containing the wrongly written characters in the text to be detected, which is not described herein again.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the upgrade method according to the present embodiment. In the application scenario of fig. 3, a user first edits a text 301 to be detected using a terminal device, selects a subject of the text to be detected as "narrative-note", and sends a text detection request including the subject of the text to be detected by clicking a "start detection" button. After receiving the request, the server performs operations such as determining a target word and a target sentence, generating word and sentence distribution information, and acquiring structure evaluation information on the text to be detected 301, and pushes a text detection result 302 including the structure evaluation information of the text to be detected and the text detection result 302 displayed by the terminal device to the terminal device.
According to the method provided by the embodiment of the application, the target words and the target sentences of the received text to be detected are determined, so that word and sentence distribution information is generated at the positions based on the target words and the target sentences, then the word and sentence distribution information is input into the structure detection model matched with the subject matter of the text to be detected, the structure evaluation information of the text to be detected is obtained, and finally the text detection result containing the structure evaluation information is pushed, so that the text does not need to be reviewed manually, and the flexibility of text detection is improved.
With further reference to fig. 4, as an implementation of the method shown in the above-mentioned figures, the present application provides an embodiment of an information pushing apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied in various electronic devices.
As shown in fig. 4, the information pushing apparatus 400 according to this embodiment includes: a receiving unit 401 configured to receive a text detection request including a text to be detected and a subject of the text to be detected; a generating unit 402, configured to determine a target word and a target sentence of the text to be detected, and generate word and sentence distribution information of the text to be detected based on a position of the target word in the text to be detected and a position of the target sentence in the text to be detected, where the target sentence is a sentence whose sentence pattern matches any preset sentence pattern in a preset sentence pattern set; an input unit 403, configured to extract a structure detection model matching the subject, and input the term distribution information into the structure detection model to obtain structure evaluation information of the text to be detected, where the structure detection model is used to represent a corresponding relationship between the term distribution information and the structure evaluation information; the pushing unit 404 is configured to push a text detection result including the structure evaluation information of the text to be detected.
In this embodiment, the receiving unit 401 may receive a text detection request sent by a client and including a text to be detected and a subject of the text to be detected.
In this embodiment, the generating unit 402 may first determine the target words and the target sentences of the text to be detected. After determining the target words and the target sentences of the text to be detected, the generating unit 402 may determine the positions of the target words and the target sentences in the text to be detected, and generate the word and sentence distribution information of the text to be detected based on the positions of the target words and the target sentences in the text to be detected.
In this embodiment, the input unit 403 may extract a structure detection model matching with the subject of the text to be detected, and input the generated word and sentence distribution information to the structure detection model to obtain the structure evaluation information of the text to be detected. The structure detection model may be used to represent a correspondence between word and sentence distribution information and structure evaluation information.
In this embodiment, the pushing unit 404 may push a text detection result including structure evaluation information of the text to be detected to a client that sends the text detection request.
In some optional implementation manners of this embodiment, the text detection result may further include a sentence pattern detection result, and the apparatus further includes a segmentation unit, a first determination unit, and a first summarization unit (not shown in the figure). The segmentation unit may be configured to segment the text to be detected sentence by sentence. The first determining unit may be configured to determine, for each sentence after segmentation, whether a wrong word is collocated in the sentence, if so, extract a recommended word collocated with the word, and generate prompt information for prompting that the sentence has a collocation problem, where the prompt information includes the recommended word. The first summarizing unit may be configured to summarize the generated prompt information to generate a sentence pattern detection result.
In some optional implementations of this embodiment, the apparatus may further include an optimization unit (not shown in the figure). The optimization unit may be configured to, for each target sentence, use a preset sentence pattern matched with the target sentence as a target preset sentence pattern, optimize the target sentence based on a preset optimization rule matched with the target preset sentence pattern, and generate prompt information for prompting optimization of the sentence.
In some optional implementation manners of this embodiment, the text detection result may further include a word detection result, and the apparatus may further include a first extraction unit, a first search unit, a second search unit, and a second summarization unit (not shown in the figure). The first extraction unit may be configured to extract keywords and verses in the text to be detected. The first searching unit may be configured to, for each extracted keyword, in response to determining that the keyword is a noun, search matching information corresponding to the keyword from a preset keyword matching information table, in response to determining that the keyword is an adjective, search a synonym of the keyword from a preset synonym table, and generate word recommendation information including the matching information or the synonym. The second searching unit may be configured to search, for each extracted verse, an associated verse associated with the verse from a preset verse library, and generate verse recommendation information including the associated verse. The second summarizing unit may be configured to summarize the generated word recommendation information and verse recommendation information, and generate a word detection result.
In some optional implementation manners of this embodiment, the text detection result may further include a wrong-word detection result, and the apparatus may further include a second extraction unit, a matching unit, and a second determination unit (not shown in the figure). The second extraction unit may be configured to extract a verse in the text to be detected. The matching unit can be configured to perform word segmentation on the text to be detected except the poetry, match each word after word segmentation with a preset word bank, and match the poetry with a preset poetry bank. The second determining unit may be configured to determine, based on the matching result, words and phrases containing wrongly written characters in the text to be detected and verses containing wrongly written characters in the text to be detected, so as to generate a wrongly written character detection result containing wrongly written character information.
In the apparatus provided in the above embodiment of the present application, the generation unit 402 determines the target words and the target sentences of the text to be detected received by the receiving unit 401, so as to generate word and sentence distribution information at positions based on the target words and the target sentences, then the input unit 403 inputs the word and sentence distribution information into the structure detection model matched with the subject of the text to be detected, so as to obtain structure evaluation information of the text to be detected, and finally the pushing unit 404 pushes the text detection result including the structure evaluation information, so that the text review does not need to be performed manually, and the flexibility of text detection is improved.
Referring now to FIG. 5, a block diagram of a computer system 500 suitable for use in implementing a server according to embodiments of the present application is shown. The server shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the method of the present application when executed by the Central Processing Unit (CPU) 501. It should be noted that the computer readable medium described herein can 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 application, 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 this application, however, 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 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 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.
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 application. 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a receiving unit, a generating unit, an input unit, and a pushing unit. The names of the units do not in some cases constitute a limitation to the units themselves, and for example, the receiving unit may also be described as a "unit that receives a text detection request including a text to be detected and a subject of the text to be detected".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: receiving a text detection request containing a text to be detected and a subject of the text to be detected; determining target words and target sentences of the text to be detected, and generating word and sentence distribution information of the text to be detected based on the position of the target words in the text to be detected and the position of the target sentences in the text to be detected; extracting a structure detection model matched with the type of the text to be detected, and inputting the word and sentence distribution information into the structure detection model to obtain structure evaluation information of the text to be detected; and pushing a text detection result containing the structure evaluation information of the text to be detected.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
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| CN114818732A (en) * | 2022-05-19 | 2022-07-29 | 北京百度网讯科技有限公司 | Text content evaluation method, related device and computer program product |
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| JP2013073282A (en) * | 2011-09-26 | 2013-04-22 | Fuji Xerox Co Ltd | Information processor and program |
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| CN1886767A (en) * | 2003-11-28 | 2006-12-27 | 语言的森林有限公司 | Essay Assessment and Grading Devices |
| US10380225B1 (en) * | 2014-07-15 | 2019-08-13 | Google Llc | Systems and methods for layout transformation of document content |
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