CN111523289A - Text format generation method, device, equipment and readable medium - Google Patents
Text format generation method, device, equipment and readable medium Download PDFInfo
- Publication number
- CN111523289A CN111523289A CN202010334143.1A CN202010334143A CN111523289A CN 111523289 A CN111523289 A CN 111523289A CN 202010334143 A CN202010334143 A CN 202010334143A CN 111523289 A CN111523289 A CN 111523289A
- Authority
- CN
- China
- Prior art keywords
- text
- target text
- preset
- label
- text format
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
- G06F40/103—Formatting, i.e. changing of presentation of documents
- G06F40/106—Display of layout of documents; Previewing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
Abstract
The embodiment of the specification discloses a text format generation method, a text format generation device, text format generation equipment and a computer readable medium. The scheme comprises the following steps: acquiring a target text; extracting key words in the target text; determining a label of the target text based on the extracted keywords; and determining the text format of the target text according to the label of the target text based on a mapping relation library of a preset label and a preset text format.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a text format generation method, apparatus, device, and computer readable medium.
Background
At present, internet technology is developed vigorously, and users can receive various information through terminals such as smart phones and computers. When an information provider pushes information to a user, the specific display mode of the pushed information greatly influences the receiving condition of the user on the information.
For example, in a scene of recommending marketing, a good case presentation form can effectively highlight the emphasis of case content, attract the eyes of users, and obtain better user conversion efficiency. However, in most of the recommended scenes at present, the format of the document is invariable, the content of the document cannot be highlighted well, and the presentation effect of the document is not good.
Disclosure of Invention
In view of this, embodiments of the present application provide a text format generation method, apparatus, device and computer readable medium, which are used to highlight text content for presentation, so that the presentation effect of the document is better.
In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:
the text format generation method provided by the embodiment of the specification comprises the following steps: acquiring a target text; extracting key words in the target text; determining a label of the target text based on the extracted keywords; and determining the text format of the target text according to the label of the target text based on a mapping relation library of a preset label and a preset text format.
An embodiment of the present specification provides a text format generating apparatus, including: the target text acquisition module is used for acquiring a target text; the keyword extraction module is used for extracting keywords in the target text; a label determining module, configured to determine a label of the target text based on the extracted keyword; and the text format determining module is used for determining the text format of the target text according to the label of the target text based on a mapping relation library of a preset label and a preset text format.
An embodiment of the present specification provides a text format generating apparatus, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a target text; extracting key words in the target text; determining a label of the target text based on the extracted keywords; and determining the text format of the target text according to the label of the target text based on a mapping relation library of a preset label and a preset text format.
The embodiments of the present specification provide a computer readable medium, on which computer readable instructions are stored, where the computer readable instructions are executable by a processor to implement the text format generating method described in any one of the foregoing embodiments.
The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects: the method comprises the steps of firstly obtaining a target text to be displayed, then extracting key words in the target text, determining a label corresponding to the target text based on the extracted key words, and then determining a proper text format of the target text according to a preset mapping relation between a preset label and the preset text format. According to the scheme, the text content of the target text is analyzed, so that the text content is displayed in a mode corresponding to the text content, the display format of the text is more fit with the text content, the text content can be better highlighted, the text display effect is better, and the understanding and the interest of a user on the file can be better promoted.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flowchart of a text format generation method provided in an embodiment of the present specification;
fig. 2 is a schematic diagram of a specific application scenario of a text format generation method provided in an embodiment of the present specification;
fig. 3 is a schematic structural diagram of a text format generating apparatus corresponding to fig. 1 provided in an embodiment of the present specification;
fig. 4 is a schematic structural diagram of a text format generating device corresponding to fig. 1 provided in an embodiment of this specification.
Detailed Description
The delivery of paperwork is an important means in, for example, a recommended marketing scenario. The good case display form can effectively highlight the key points of the case contents, attract the eyeballs of the user and obtain better conversion efficiency. In most recommended scenes at present, the presentation form of the document is invariable, that is, the presentation form of the document is the same for different document contents and different users. The existing text format for displaying the files cannot effectively highlight the contents of different files, and is difficult to promote the understanding and interest of users on the contents of the files. In order to better highlight the text content of the file and promote the understanding and interest of the user to the file, the application provides a method for generating the text format of the file based on text content mining.
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, the terms first, second, etc. are used to describe various information, parameters, fields, instructions, terminals, etc., but these information, parameters, fields, instructions, terminals, etc. should not be limited by these terms. These terms are used to distinguish one information, parameter, field, instruction, terminal from another information, parameter, field, instruction, terminal. Thus, a first information, parameter, field, instruction, terminal discussed below could also be termed a second information, parameter, field, instruction, terminal without departing from the teachings of the present disclosure.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a text format generation method provided in an embodiment of the present specification. From the viewpoint of the program, the execution subject of the flow may be a program installed in an application server or an application terminal.
As shown in fig. 1, the process may include the following steps:
step 102: and acquiring a target text.
The target text may be a document planned to be presented to the user, wherein the document is a text for content recommendation. For example, the document may be a recommended word, a recommended title, etc. In some cases, the document may also be a paragraph, a short, etc.
The target text may be a word, a sentence, etc. For example, a word form of target text may be "ant forest", "car insurance", "red envelope", etc. For example, a target text in the form of a sentence may be "hungry how: enjoy the surrounding delicacies "," reserve gold: your emergency wallet ", etc.
Step 104: and extracting key words in the target text.
In an embodiment, specifically, the extracting of the keyword in the target text may specifically include: performing word segmentation on the target text to obtain a word segmentation set corresponding to the target text; extracting keywords from the word segmentation set to obtain a keyword set corresponding to the target text, wherein the keyword set comprises at least one keyword.
Specifically, existing word segmentation technology may be adopted to segment the target text. The word segmentation technology refers to a process of recombining continuous word sequences into word sequences according to a certain specification. As an example, Natural Language Processing (NLP) algorithm word segmentation technology may be adopted to segment the target text, and accordingly, in the word segmentation process, a common word segmentation packet in NLP may be adopted. The technique of segmenting the target text is not limited to this example, and embodiments of the present application may be implemented using any segmentation technique in the prior art.
For the target text in the form of words, the word segmentation set obtained after word segmentation can contain the original words. For example, for the target texts in the form of words, "ant forest", "car insurance" and "red packet", the corresponding participle sets obtained after the participle respectively include "ant forest", "car insurance" and "red packet".
For a target text in a sentence form, a sentence can be divided into a plurality of comprehensible words through a word segmentation technology, and the obtained word segmentation set after word segmentation can contain the split comprehensible words in the sentence. For example, for a target text in sentence form, "hungry how: the users can enjoy the surrounding gourmet food completely, and the participle set obtained after the participle processing can contain 'how hungry, how exhausted, enjoying, surrounding and gourmet food'; for "spare gold: your emergency wallet, the set of participles obtained after the participles can contain "spare money, your, emergency, wallet".
The segmented word set obtained by the segmentation technique may include both keywords having attribute features such as "how hungry", "spare money", and the like, and non-keywords having no obvious attribute features such as "your", "best", "shared", and the like.
In order to improve the recognition efficiency and the recognition accuracy of text attribute feature recognition in the subsequent steps, a keyword set can be further screened out from the divided word segmentation set, that is, keywords in the word segmentation set are extracted. More specifically, important words related to the purpose can be extracted based on the word segmentation result. In an embodiment, the keyword set may be extracted from the participle set using, for example, a keyword extraction algorithm in NLP.
For example, for the word segmentation set "how hungry, what, surrounding, and food are hungry", the corresponding keyword set may include "how hungry, food" and the like; for the participle set 'reserve money, your, emergency, purse', the corresponding keyword set may include 'reserve money, purse'.
Step 106: and determining the label of the target text based on the extracted keywords.
In an embodiment, the determining the tag of the target text based on the extracted keyword may specifically include: determining a tag set of the target text based on the keyword set corresponding to the target text, wherein each keyword in the keyword set corresponds to at least one tag in the tag set.
Alternatively, multiple tags in the keyword set may correspond to the same tag, and for example, if the keyword set includes "hungry and food", both of the keywords may correspond to the tag "food". Alternatively, one keyword in the keyword set may correspond to a plurality of tags, and for example, if the keyword set includes "car insurance", the keyword may correspond to both tags "car present" and "finance".
In an embodiment, the determining the tag of the target text based on the extracted keyword may specifically include: and inputting the keywords into a label determination model trained in advance to obtain a label set corresponding to the target text.
Specifically, the tag determination model may be various, and may be, for example, a model for determining attributes of a scene, a model for determining attributes of an audience crowd, a model for determining attributes of emotion, a model for determining attributes of event degrees, a model for determining attributes of regions, and the like.
The tag determination model may include one or more.
Optionally, when the tag determination model is multiple, the determining the tag of the target text based on the extracted keyword may specifically include: inputting the keyword into a pre-trained first label determination model to obtain a first label subset; inputting the keywords into a pre-trained second label determination model to obtain a second label subset; and taking the labels in the first label subset and the second label subset as the labels in the label set corresponding to the target text.
As an example, optionally, the keyword may be input into a third label determination model trained in advance to obtain a third label subset, and a label in the third label subset is also used as a label in the label set corresponding to the target text. It is to be understood that the number of tag determination models is not limited thereto, and may be set as needed.
The first tag model, the second tag model, the third tag model, etc. may be selected from, for example, a scene attribute tag model, an audience population attribute tag model, an emotion attribute tag model, an event degree attribute tag model, a region attribute tag model, etc., but are not limited thereto.
For the scenario attribute tag model, the determined tags thereof may be, for example, "public welfare", "education", "mother-and-baby", "activity", "holiday", "cate", "fitness", "health", "finance", "life", "sleep", "shopping", "reading", "home", and the like, without being limited thereto. For the audience population attribute label model, the determined labels may be, for example, but not limited to, "mother and baby", "car", "male", "female", "child", "scale seat", and the like. For the emotion attribute tag model, the tags determined by the emotion attribute tag model may be, for example, "active", "passive", "neutral", and the like, without limitation. For the event degree attribute label model, the determined label may be, for example, but not limited to, "severe", "urgent", "normal", and the like. For the region attribute label model, the label determined by the region attribute label model may be, for example, "northeast", "west", "coastal region", "beijing", and the like, but is not limited thereto.
Any subset of tags may include one or more tags. For example, for a keyword set "hungry, gourmet, and five", the keyword set is input into the scene attribute label model, and the obtained corresponding label subset may include "gourmet, holiday". In some cases, any subset of tags may be an empty set. When the keyword in the target text does not have the event degree attribute, if the keyword of the target text is input into the event degree attribute tag model, the obtained corresponding tag subset may be an empty set. For example, for the keyword set "hungry, food, five one", which is input into the event degree attribute tag model, the resulting corresponding tag subset may be an empty set.
It should be noted that although each label model corresponds to different attributes, labels corresponding to different models may overlap, for example, a label of "mother and infant" belongs to both a scene attribute label and an audience population attribute label.
In practical applications, the process of determining the label of the target text based on the extracted keyword may be optionally specifically divided into two stages: firstly, mining the content of a target text based on keywords; and then, marking the excavation result by utilizing labels in a label library which is constructed in advance.
Specifically, content mining of the target text may include, but is not limited to, performing sentiment analysis, regional analysis, business attribute analysis, audience segment analysis, and the like. Wherein, the emotion analysis is to analyze, process, induce and reason about subjective texts with emotion colors; audience segment analysis refers to analysis of a population of information recipients; business attribute analysis refers to a compartmentalized analysis of business types.
Specifically, the above-mentioned pre-constructed tag library may be a library containing tags with different attributes, for example, tags "positive", "negative", etc. which may contain emotions, tags "mother and baby", "car", "male", "female", "child", "scale seat", etc. which may contain people; labels "positive", "negative", etc. of the event degree class may be included.
In practical application, a content mining technology can be adopted firstly, mining analysis of scene, emotion, audience population and other aspects is carried out on a target text based on keywords, and then marking operation is carried out on the target text correspondingly by using corresponding scene tags, emotion tags, audience population tags and the like in a pre-established tag library according to an analysis result.
For ease of understanding, some examples of tags obtained based on keywords are given below: the ant forest comprises a scene attribute label 'public welfare', 'vehicle insurance' comprises a directional crowd label 'with a vehicle', 'red packet' comprises a scene label 'activity' and an emotion label 'active', 'hungry and cate' comprise a scene label 'cate', 'reserve money and small wallet' comprise a scene label 'finance', 'anti-epidemic situation' comprises an emotion label 'passive' and 'emergency'.
Step 108: and determining the text format of the target text according to the label of the target text based on a mapping relation library of a preset label and a preset text format.
And the mapping relation library stores the mapping relation between the preset label and the preset text format. The preset tags may be the tags in a pre-constructed tag library as mentioned above. The preset text format may be predetermined according to the user historical behavior information.
Specifically, before determining the text format of the target text according to the label of the target text based on the mapping relationship library between the preset label and the preset text format, the method may further include: determining at least one preset label and constructing a preset label set; determining a corresponding preset text format for each preset label in the preset label set; and constructing a mapping relation library of the preset labels and the preset text format according to each preset label and the corresponding preset text format thereof.
Specifically, the corresponding preset text format is determined for each preset tag in the preset tag set, where the determination may be performed by a developer according to experience or according to historical behavior information of a user. When the determination is performed according to the user historical behavior information, a specific determination method may include: acquiring historical behavior information of a user, wherein the historical behavior information of the user reflects the acceptance degree of the user to a text which is displayed in a preset text format and corresponds to the preset label; and determining at least one preset text format corresponding to each preset label based on the historical behavior information of the user. The acceptance degree may correspond to, for example, a ratio of a user amount for performing effective feedback on a target text in a preset text format among a large number of counted users, where the effective feedback may include performing a click operation on the target text, and the like.
Optionally, in an embodiment of the present application, the text format may include text color, text effect, text font, and the like. The text color may be simple red, yellow, green, etc., or may be an RGB color. The text effects may include bolding, underlining, italics, and the like. The text font may include a song style, a regular style, and the like. The text format is not limited to the examples given herein, e.g., text effects may also have dynamic effects (e.g., blinking, becoming larger), etc.
For example, an "ant forest" contains a "public welfare" tag, and in the mapping relation library of the preset tag and the preset text format, the corresponding color of the "public welfare" tag is "green", and the "ant forest" may correspond to the "green" text format. If the "red envelope" includes an "active" label and a "finance" label, and the mapping relation between the preset label and the preset text format includes "active-red/orange" and "finance-red", the "red envelope" may correspond to the "red" text format. For another example, the "anti-epidemic situation" includes an "emergency" tag, and the mapping relationship between the preset tag and the preset text format includes "emergency-black/red" and "emergency-bold", so that the anti-epidemic situation "may correspond to the" black/red, bold "text format.
The method in fig. 1 determines the text format for displaying the text content corresponding to the text content by analyzing the text content of the target text, so that the display format of the text is more suitable for the text content, the text content can be better highlighted, that is, the text display effect is better, and the understanding and interest of the user on the file can be better promoted.
Based on the process of fig. 1, some specific embodiments of the process are also provided in the examples of this specification, which are described below.
In step 108, after determining the text format of the target text, the method may further include: and step 110, displaying the target text based on the text format.
Specifically, the execution subject of step 110 may be a program loaded on the application terminal. If steps 102 to 108 are executed on the server, before step 110, the method may further include that the server sends information containing the text format to the application terminal.
Before presenting the target text based on the text format (step 110), the method may further include: and selecting a target text format for displaying the target text from a text format set corresponding to the label set of the target text according to a preset rule.
For example, a mapping relationship of, for example, tags such as "public welfare-green/blue", "education-blue", "activity-red/orange", "cate-red/orange", "health-blue/green", "finance-red", "sleep-dark", "shopping-red/orange", "reading-dark", "passive-black", "severe-black/red", "urgent-black/red" may be stored in the mapping relationship library with a text format; "Severe-bolded", "Emergency-bolded", etc.
In the mapping relation library, one preset label may correspond to one or more preset text formats, for example, the label "public welfare" may correspond to blue or green, and at this time, blue and green may be determined as the text format corresponding to the label at the same time. When the target text is displayed subsequently, any one of the target text and the target text can be displayed, or the target text and the target text can be selected by combining other preset rules.
Optionally, the selecting, according to the preset rule, one target text format from the text format set corresponding to the tag set of the target text may specifically include: acquiring a page background color of a page to be displayed of a target text and/or a screen color system of a user terminal; and selecting a target text format for displaying the target text from the text format set based on the page background color of the page to be displayed and/or the screen color system of the user terminal.
In practical applications, there may be different background colors for different APPs, or in different pages in the same APP; in addition, in practical use, the user may adjust the screen color system according to the change of the external light, for example, a white screen background is used in the daytime, and a black screen background is used at night. In order to make the display effect of the target text better, the text format for final display can be selected from the alternative text formats according to the background color of the page to be displayed of the target text and/or the screen color system of the user terminal. For example, the candidate text color with high contrast may be selected as the color finally used for displaying the target text by calculating the contrast of the candidate text color with the page background color and/or the screen color system.
Optionally, the selecting, according to the preset rule, one target text format from the text format set corresponding to the tag set of the target text may specifically include: acquiring use preference information of a user; and selecting a target text format for displaying the target text from the text format set according to the use preference information of the user.
The usage preference information of the user may be obtained by analyzing historical behavior data of the user. For example, the user's preference for different colors may be inferred from the user's setting information for pages, the user's usage information for different colored texts and pictures, etc., and from the user's historical feedback rate for various colored texts, etc., and the color ultimately used to present the target text may be selected from alternative color formats based on the user's color preference.
In the above, it is only explained from the perspective of text color how to select a text format for presentation finally from alternative text formats based on a preset rule, and actually, for a text effect, a text font, and the like, the selection of the final text format may also be performed based on a preset rule (for example, according to the quality of the final presentation effect and the preference of the user), and details are not described herein again.
In the scheme of the embodiment of the application, the target text is firstly segmented, the keywords in the target text are extracted, the text contents of the keywords are obtained, deeper content mining analysis is carried out, and corresponding tags are marked. Then, by associating the content with the tag, and then associating the tag with the text format (color, font, etc.), the correspondence between the text content and the text format is obtained. And further, combining the text format set of the target text content with a text control background color and/or a mobile phone screen color system, dynamically generating the text format of the target text and dynamically displaying the target text. The dynamic state may include, for example, presenting the target text in different forms for different users and user terminals; for the same user terminal, the target text can be presented in different forms according to the current setting of the user terminal.
The scheme of the embodiment of the application combines the text content of the file to generate the text format (color, font form and the like) of the file, fully considers the influence of the text format on the file effect, and promotes the interest and understanding of the file of the user. And dynamic putting is realized, namely, after the text format library is generated, the final text format of the file can be dynamically generated by combining the background color of the text control, the mobile phone environment and the like.
In order to make the solution of the present application clearer, a specific application scenario is provided below. Fig. 2 is a schematic diagram of a specific application scenario of a text format generation method provided in an embodiment of the present specification.
As shown in fig. 2, firstly, the NLP algorithm is used to segment words, and a common segmentation packet in NLP can be used specifically; then, extracting keywords by adopting NLP (non-line segment process), wherein a common keyword extraction algorithm in the NLP can be specifically used; then, content mining is carried out, including but not limited to emotion mining, region mining, business attribute mining, audience group mining and the like; meanwhile, a set of content tag library is prepared, such as tags of emotion types such as "active" and "passive", tags of people types such as "mother and baby", "with car", "male", "female", "child", "scale seat" and the like, and tags of event degrees such as "active" and "passive" and the like; meanwhile, a set of color library can be simple red, yellow, green and the like, and can also be RGB and the like; meanwhile, a set of tag-font color matching libraries, such as "public welfare-green/blue", "education-blue", "activity-red/orange", "food-red/orange", "health-blue/green", "finance-red", "sleep-dark", "shopping-red/orange", "reading-dark", "passive-black", "severe-black/red", "urgent-black/red", "severe-bold", "urgent-bold", and "urgent-bold", are prepared in correspondence with the content tag library and the color library; then, adding label attributes such as emotion labels such as 'active' and 'passive', etc., or group labels such as 'mother and baby' and 'car-in', etc., or event degree labels such as 'serious' and 'emergency', etc., to the mined content; the mapping relation of the content and the font color can be generated through the mapping relation of the content and the label and the mapping relation of the color font; further, based on the mapping relationship between the content and the font color, the color and the font of the file can be dynamically generated by combining the text control and the screen color.
Based on the same idea, the embodiment of the present specification further provides a device corresponding to the above method. Fig. 3 is a schematic structural diagram of a text format generating apparatus corresponding to fig. 1 provided in an embodiment of the present specification.
As shown in fig. 3, the apparatus may include:
a target text obtaining module 302, configured to obtain a target text;
a keyword extraction module 304, configured to extract keywords in the target text;
a tag determination module 306, configured to determine a tag of the target text based on the extracted keyword;
and the text format determining module 308 is configured to determine the text format of the target text according to the label of the target text based on a mapping relation library of preset labels and preset text formats.
According to an embodiment, the keyword extraction module 304 may specifically include: the word segmentation set generation unit is used for performing word segmentation on the target text to obtain a word segmentation set corresponding to the target text; and the keyword set generating unit is used for extracting keywords from the word segmentation set to obtain a keyword set corresponding to the target text, wherein the keyword set comprises at least one keyword.
Accordingly, the tag determination module 306 may be specifically configured to: determining a tag set of the target text based on the keyword set corresponding to the target text, wherein each keyword in the keyword set corresponds to at least one tag in the tag set.
Optionally, the tag determining module 306 may be specifically configured to: and inputting the keywords into a label determination model trained in advance to obtain a label set corresponding to the target text.
According to an embodiment, the apparatus may further include a mapping relation library construction module configured to construct a mapping relation library before determining the text format of the target text according to the tag of the target text based on the mapping relation library of the preset tag and the preset text format. Specifically, the mapping relation library building module includes: the preset label set constructing unit is used for determining at least one preset label and constructing a preset label set; a preset text format determining unit, configured to determine, for each preset tag in the preset tag set, a corresponding preset text format; and the mapping relation library construction unit is used for constructing a mapping relation library of the preset labels and the preset text formats according to each preset label and the corresponding preset text format.
Optionally, the preset text format determining unit may be specifically configured to: acquiring historical behavior information of a user, wherein the historical behavior information of the user reflects the acceptance degree of the user to a text which is displayed in a preset text format and corresponds to the preset label; and determining at least one preset text format corresponding to each preset label based on the historical behavior information of the user.
Optionally, the text format may include text color, text effects including bold, underlining, italics, and text font.
According to an embodiment, the apparatus may further comprise: and a target text display module 310, configured to display the target text based on the text format.
Optionally, the apparatus may further include: and the target text format selection module is used for selecting a target text format for displaying the target text from the text format set corresponding to the label set of the target text according to a preset rule.
Optionally, the target text format selecting module may specifically include: the background color acquisition unit is used for acquiring the page background color of the page to be displayed of the target text and/or the screen color system of the user terminal; and the target text format determining unit is used for selecting a target text format for displaying the target text from the text format set based on the page background color of the page to be displayed and/or the screen color system of the user terminal.
Optionally, the target text format selecting module may specifically include: a user use preference information acquisition unit for acquiring use preference information of a user; and the target text format determining unit is used for selecting a target text format for displaying the target text from the text format set according to the use preference information of the user.
It will be appreciated that the modules described above refer to computer programs or program segments for performing a certain function or functions. In addition, the distinction between the above-described modules does not mean that the actual program code must also be separated.
Based on the same idea, the embodiment of the present specification further provides a device corresponding to the above method.
Fig. 4 is a schematic structural diagram of a text format generating device corresponding to fig. 1 provided in an embodiment of this specification. As shown in fig. 4, the apparatus 400 may include:
at least one processor 410; and the number of the first and second groups,
a memory 430 communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory 430 stores instructions 420 executable by the at least one processor 410 to enable the at least one processor 410 to:
acquiring a target text;
extracting key words in the target text;
determining a label of the target text based on the extracted keywords;
and determining the text format of the target text according to the label of the target text based on a mapping relation library of a preset label and a preset text format.
Based on the same idea, the embodiments of the present specification further provide a computer-readable medium corresponding to the above method, where the computer-readable medium has stored thereon computer-readable instructions, and the computer-readable instructions are executable by a processor to implement the text format generation method described in any of the above embodiments.
While particular embodiments of the present specification have been described above, in some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the device and apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.
The apparatus, the device, and the method provided in the embodiments of the present specification are corresponding, and therefore, the apparatus and the device also have beneficial technical effects similar to those of the corresponding method, and since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus and device are not described again here.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean expression Language), ahdl (alternate Language Description Language), traffic, pl (core universal programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), and vhjraygurg-Language (Hardware Description Language), which is currently used by Hardware-Language. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, AtmelAT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory computer readable media (transmyedia) such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (24)
1. A text format generation method, comprising:
acquiring a target text;
extracting key words in the target text;
determining a label of the target text based on the extracted keywords;
and determining the text format of the target text according to the label of the target text based on a mapping relation library of a preset label and a preset text format.
2. The method according to claim 1, wherein the extracting the keywords in the target text specifically includes:
performing word segmentation on the target text to obtain a word segmentation set corresponding to the target text;
extracting keywords from the word segmentation set to obtain a keyword set corresponding to the target text, wherein the keyword set comprises at least one keyword.
3. The method according to claim 2, wherein determining the label of the target text based on the extracted keyword specifically comprises:
determining a tag set of the target text based on the keyword set corresponding to the target text, wherein each keyword in the keyword set corresponds to at least one tag in the tag set.
4. The method according to claim 1, wherein the determining the label of the target text based on the extracted keyword specifically includes:
and inputting the keywords into a label determination model trained in advance to obtain a label set corresponding to the target text.
5. The method of claim 1, wherein before determining the text format of the target text according to the label of the target text based on the mapping relation library of the preset label and the preset text format, the method further comprises:
determining at least one preset label and constructing a preset label set;
determining a corresponding preset text format for each preset label in the preset label set;
and constructing a mapping relation library of the preset labels and the preset text format according to each preset label and the corresponding preset text format thereof.
6. The method according to claim 5, wherein the determining, for each preset tag in the preset tag set, a corresponding preset text format specifically comprises:
acquiring historical behavior information of a user, wherein the historical behavior information of the user reflects the acceptance degree of the user to a text which is displayed in a preset text format and corresponds to the preset label;
and determining at least one preset text format corresponding to each preset label based on the historical behavior information of the user.
7. The method of claim 1, wherein the text format comprises text color, text effects, and text font, the text effects comprising bold, underlining, italics.
8. The method of claim 1, after determining the text format of the target text, further comprising:
and displaying the target text based on the text format.
9. The method of claim 8, wherein prior to presenting the target text based on the text format, further comprising:
and selecting a target text format for displaying the target text from a text format set corresponding to the label set of the target text according to a preset rule.
10. The method according to claim 9, wherein selecting a target text format from the text format set corresponding to the tag set of the target text according to a preset rule specifically includes:
acquiring a page background color of a page to be displayed of a target text and/or a screen color system of a user terminal;
and selecting a target text format for displaying the target text from the text format set based on the page background color of the page to be displayed and/or the screen color system of the user terminal.
11. The method according to claim 9, wherein selecting a target text format from the text format set corresponding to the tag set of the target text according to a preset rule specifically includes:
acquiring use preference information of a user;
and selecting a target text format for displaying the target text from the text format set according to the use preference information of the user.
12. A text format generating apparatus comprising:
the target text acquisition module is used for acquiring a target text;
the keyword extraction module is used for extracting keywords in the target text;
a label determining module, configured to determine a label of the target text based on the extracted keyword;
and the text format determining module is used for determining the text format of the target text according to the label of the target text based on a mapping relation library of a preset label and a preset text format.
13. The apparatus according to claim 12, wherein the keyword extraction module specifically includes:
the word segmentation set generation unit is used for performing word segmentation on the target text to obtain a word segmentation set corresponding to the target text;
and the keyword set generating unit is used for extracting keywords from the word segmentation set to obtain a keyword set corresponding to the target text, wherein the keyword set comprises at least one keyword.
14. The apparatus of claim 13, wherein the tag determination module is specifically configured to:
determining a tag set of the target text based on the keyword set corresponding to the target text, wherein each keyword in the keyword set corresponds to at least one tag in the tag set.
15. The apparatus of claim 12, wherein the tag determination module is specifically configured to:
and inputting the keywords into a label determination model trained in advance to obtain a label set corresponding to the target text.
16. The apparatus according to claim 12, further comprising a mapping relation library construction module configured to construct a mapping relation library before determining a text format of the target text according to a tag of the target text based on a mapping relation library of a preset tag and a preset text format, and specifically, the mapping relation library construction module includes:
the preset label set constructing unit is used for determining at least one preset label and constructing a preset label set;
a preset text format determining unit, configured to determine, for each preset tag in the preset tag set, a corresponding preset text format;
and the mapping relation library construction unit is used for constructing a mapping relation library of the preset labels and the preset text formats according to each preset label and the corresponding preset text format.
17. The apparatus according to claim 16, wherein the preset text format determining unit is specifically configured to: acquiring historical behavior information of a user, wherein the historical behavior information of the user reflects the acceptance degree of the user to a text which is displayed in a preset text format and corresponds to the preset label; and determining at least one preset text format corresponding to each preset label based on the historical behavior information of the user.
18. The apparatus of claim 12, wherein the text format comprises text color, text effects, and text font, the text effects comprising bold, underlining, italics.
19. The apparatus of claim 12, the apparatus further comprising: and the target text display module is used for displaying the target text based on the text format.
20. The apparatus of claim 19, the apparatus further comprising: and the target text format selection module is used for selecting a target text format for displaying the target text from the text format set corresponding to the label set of the target text according to a preset rule before displaying the target text.
21. The apparatus of claim 20, wherein the target text format selection module specifically comprises:
the background color acquisition unit is used for acquiring the page background color of the page to be displayed of the target text and/or the screen color system of the user terminal;
and the target text format determining unit is used for selecting a target text format for displaying the target text from the text format set based on the page background color of the page to be displayed and/or the screen color system of the user terminal.
22. The apparatus of claim 20, wherein the target text format selection module specifically comprises:
a user use preference information acquisition unit for acquiring use preference information of a user;
and the target text format determining unit is used for selecting a target text format for displaying the target text from the text format set according to the use preference information of the user.
23. A text format generating device comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a target text;
extracting key words in the target text;
determining a label of the target text based on the extracted keywords;
and determining the text format of the target text according to the label of the target text based on a mapping relation library of a preset label and a preset text format.
24. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the text format generation method of any one of claims 1 to 11.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010334143.1A CN111523289B (en) | 2020-04-24 | 2020-04-24 | Text format generation method, device, equipment and readable medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010334143.1A CN111523289B (en) | 2020-04-24 | 2020-04-24 | Text format generation method, device, equipment and readable medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111523289A true CN111523289A (en) | 2020-08-11 |
| CN111523289B CN111523289B (en) | 2023-05-09 |
Family
ID=71903505
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010334143.1A Active CN111523289B (en) | 2020-04-24 | 2020-04-24 | Text format generation method, device, equipment and readable medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111523289B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112035408A (en) * | 2020-09-01 | 2020-12-04 | 文思海辉智科科技有限公司 | Text processing method and device, electronic equipment and storage medium |
| CN112395881A (en) * | 2020-11-27 | 2021-02-23 | 北京筑龙信息技术有限责任公司 | Material label construction method and device, readable storage medium and electronic equipment |
| CN112948677A (en) * | 2021-02-26 | 2021-06-11 | 上海携旅信息技术有限公司 | Recommendation reason determination method, system, device and medium based on comment aesthetic feeling |
| CN113051632A (en) * | 2021-03-30 | 2021-06-29 | 厦门极致互动网络技术股份有限公司 | Photoshop-based color derivation method |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005234772A (en) * | 2004-02-18 | 2005-09-02 | Fuji Xerox Co Ltd | Documentation management system and method |
| US20100076991A1 (en) * | 2008-09-09 | 2010-03-25 | Kabushiki Kaisha Toshiba | Apparatus and method product for presenting recommended information |
| CN104537088A (en) * | 2014-12-31 | 2015-04-22 | 百度在线网络技术（北京）有限公司 | Information showing method and device |
| CN105353957A (en) * | 2015-10-28 | 2016-02-24 | 深圳市金立通信设备有限公司 | Information display method and terminal |
| CN105630454A (en) * | 2016-01-29 | 2016-06-01 | 广东欧珀移动通信有限公司 | Information display method and terminal equipment |
| CN105938477A (en) * | 2015-03-03 | 2016-09-14 | 国际商业机器公司 | Method and system for consolidating and formatting search results |
| CN106055677A (en) * | 2016-06-07 | 2016-10-26 | 微梦创科网络科技（中国）有限公司 | Method and device for displaying content aggregation page in information stream |
| CN106250088A (en) * | 2016-08-03 | 2016-12-21 | 青岛海信电器股份有限公司 | Text display method and device |
| CN106294642A (en) * | 2016-08-03 | 2017-01-04 | 天梯头条传媒（苏州）有限公司 | There is the information issuing system of greek version function |
| US9779065B1 (en) * | 2013-08-29 | 2017-10-03 | Google Inc. | Displaying graphical content items based on textual content items |
| US20180150438A1 (en) * | 2016-11-21 | 2018-05-31 | Alibaba Group Holding Limited | Information displayed while information is scrolled on a terminal screen |
| CN108470055A (en) * | 2018-03-15 | 2018-08-31 | 维沃移动通信有限公司 | A kind of display methods and mobile terminal of text message |
| US20180268053A1 (en) * | 2017-03-14 | 2018-09-20 | Accenture Global Solutions Limited | Electronic document generation using data from disparate sources |
| CN109033061A (en) * | 2017-06-09 | 2018-12-18 | 北大方正集团有限公司 | Composition method and composing device |
| CN109240884A (en) * | 2018-08-30 | 2019-01-18 | 郑州云海信息技术有限公司 | A kind of system file display methods and device |
| CN109710863A (en) * | 2018-11-27 | 2019-05-03 | 平安科技（深圳）有限公司 | Information conversion method, device, computer equipment and storage medium |
| US20190155884A1 (en) * | 2017-11-20 | 2019-05-23 | Adobe Inc. | Dynamic Digital Document Visual Aids in a Digital Medium Environment |
| CN110399339A (en) * | 2019-06-18 | 2019-11-01 | 平安科技（深圳）有限公司 | File classifying method, device, equipment and the storage medium of knowledge base management system |
| CN110728983A (en) * | 2018-07-16 | 2020-01-24 | 科大讯飞股份有限公司 | Information display method, device, equipment and readable storage medium |
| CN110781289A (en) * | 2019-11-07 | 2020-02-11 | 北京邮电大学 | Text visualization method for reserving unstructured text semantics |
| CN110889279A (en) * | 2018-08-15 | 2020-03-17 | 珠海金山办公软件有限公司 | Method and device for displaying display information |
-
2020
- 2020-04-24 CN CN202010334143.1A patent/CN111523289B/en active Active
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005234772A (en) * | 2004-02-18 | 2005-09-02 | Fuji Xerox Co Ltd | Documentation management system and method |
| US20100076991A1 (en) * | 2008-09-09 | 2010-03-25 | Kabushiki Kaisha Toshiba | Apparatus and method product for presenting recommended information |
| US9779065B1 (en) * | 2013-08-29 | 2017-10-03 | Google Inc. | Displaying graphical content items based on textual content items |
| CN104537088A (en) * | 2014-12-31 | 2015-04-22 | 百度在线网络技术（北京）有限公司 | Information showing method and device |
| CN105938477A (en) * | 2015-03-03 | 2016-09-14 | 国际商业机器公司 | Method and system for consolidating and formatting search results |
| CN105353957A (en) * | 2015-10-28 | 2016-02-24 | 深圳市金立通信设备有限公司 | Information display method and terminal |
| CN105630454A (en) * | 2016-01-29 | 2016-06-01 | 广东欧珀移动通信有限公司 | Information display method and terminal equipment |
| CN106055677A (en) * | 2016-06-07 | 2016-10-26 | 微梦创科网络科技（中国）有限公司 | Method and device for displaying content aggregation page in information stream |
| CN106250088A (en) * | 2016-08-03 | 2016-12-21 | 青岛海信电器股份有限公司 | Text display method and device |
| CN106294642A (en) * | 2016-08-03 | 2017-01-04 | 天梯头条传媒（苏州）有限公司 | There is the information issuing system of greek version function |
| US20180150438A1 (en) * | 2016-11-21 | 2018-05-31 | Alibaba Group Holding Limited | Information displayed while information is scrolled on a terminal screen |
| US20180268053A1 (en) * | 2017-03-14 | 2018-09-20 | Accenture Global Solutions Limited | Electronic document generation using data from disparate sources |
| CN109033061A (en) * | 2017-06-09 | 2018-12-18 | 北大方正集团有限公司 | Composition method and composing device |
| US20190155884A1 (en) * | 2017-11-20 | 2019-05-23 | Adobe Inc. | Dynamic Digital Document Visual Aids in a Digital Medium Environment |
| CN108470055A (en) * | 2018-03-15 | 2018-08-31 | 维沃移动通信有限公司 | A kind of display methods and mobile terminal of text message |
| CN110728983A (en) * | 2018-07-16 | 2020-01-24 | 科大讯飞股份有限公司 | Information display method, device, equipment and readable storage medium |
| CN110889279A (en) * | 2018-08-15 | 2020-03-17 | 珠海金山办公软件有限公司 | Method and device for displaying display information |
| CN109240884A (en) * | 2018-08-30 | 2019-01-18 | 郑州云海信息技术有限公司 | A kind of system file display methods and device |
| CN109710863A (en) * | 2018-11-27 | 2019-05-03 | 平安科技（深圳）有限公司 | Information conversion method, device, computer equipment and storage medium |
| CN110399339A (en) * | 2019-06-18 | 2019-11-01 | 平安科技（深圳）有限公司 | File classifying method, device, equipment and the storage medium of knowledge base management system |
| CN110781289A (en) * | 2019-11-07 | 2020-02-11 | 北京邮电大学 | Text visualization method for reserving unstructured text semantics |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112035408A (en) * | 2020-09-01 | 2020-12-04 | 文思海辉智科科技有限公司 | Text processing method and device, electronic equipment and storage medium |
| CN112035408B (en) * | 2020-09-01 | 2023-10-31 | 文思海辉智科科技有限公司 | Text processing method, device, electronic equipment and storage medium |
| CN112395881A (en) * | 2020-11-27 | 2021-02-23 | 北京筑龙信息技术有限责任公司 | Material label construction method and device, readable storage medium and electronic equipment |
| CN112948677A (en) * | 2021-02-26 | 2021-06-11 | 上海携旅信息技术有限公司 | Recommendation reason determination method, system, device and medium based on comment aesthetic feeling |
| CN112948677B (en) * | 2021-02-26 | 2023-11-03 | 上海携旅信息技术有限公司 | Recommendation reason determining method, system, equipment and medium based on comment aesthetic feeling |
| CN113051632A (en) * | 2021-03-30 | 2021-06-29 | 厦门极致互动网络技术股份有限公司 | Photoshop-based color derivation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111523289B (en) | 2023-05-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111523289B (en) | Text format generation method, device, equipment and readable medium | |
| Conway et al. | Machine learning for hackers | |
| US11481832B2 (en) | Electronic apparatus and method for summarizing content thereof | |
| CN114155543B (en) | Neural network training method, document image understanding method, device and equipment | |
| CN109992771B (en) | Text generation method and device | |
| CN110674188A (en) | Feature extraction method, device and equipment | |
| US20230386238A1 (en) | Data processing method and apparatus, computer device, and storage medium | |
| CN115203539B (en) | Media content recommendation method, device, equipment and storage medium | |
| US20160188556A1 (en) | Adaptive electronic document | |
| US20220253288A1 (en) | Natural solution language | |
| CN111046304B (en) | Data searching method and device | |
| US20230126821A1 (en) | Systems, devices and methods for the dynamic generation of dialog-based interactive content | |
| CN106204695A (en) | The edit methods of a kind of 3D animation and device | |
| CN115769219A (en) | Generating cascading text formatting for electronic documents and displays | |
| CN117131272A (en) | Artificial intelligence content generation method, model and system | |
| CN115238250B (en) | Model processing method, device and equipment | |
| CN108519986B (en) | Webpage generation method, device and equipment | |
| JP2018508080A (en) | Input data processing method, apparatus, device, and non-executable computer storage medium | |
| CN114926687A (en) | Training method and device for classification model | |
| CN111523995B (en) | Method, device and equipment for determining characteristic value of model migration | |
| CN111259662B (en) | Method and device for processing text | |
| CN108804603B (en) | Man-machine written dialogue method and system, server and medium | |
| CN113254635B (en) | Data processing method, device and storage medium | |
| CN116795972B (en) | Model training method and device, storage medium and electronic equipment | |
| Yang et al. | Sentiment Sentence Construction Algorithm of Newly-Coined Words and Emoticons Dictionary for Social Data Opinion Analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |