CN111950259A

CN111950259A - Text display method, device, equipment and storage medium

Info

Publication number: CN111950259A
Application number: CN202010843751.5A
Authority: CN
Inventors: 卓民; 杨楠
Original assignee: Shenzhen Kaniu Technology Co ltd
Current assignee: Shenzhen Kaniu Technology Co ltd
Priority date: 2020-08-20
Filing date: 2020-08-20
Publication date: 2020-11-17

Abstract

The embodiment of the invention discloses a text display method, a text display device, text display equipment and a storage medium. The method comprises the following steps: acquiring a target text; inputting sentences in the same paragraph in the target text into a pre-trained neural network model in pairs in sequence to obtain the similarity between sentences in the same paragraph; and displaying the target text according to the similarity between sentences in the same paragraph. The embodiment of the invention realizes filtering of inconsistent contents in the text.

Description

Text display method, device, equipment and storage medium

Technical Field

The embodiment of the invention relates to a text technology, in particular to a text display method, a text display device, text display equipment and a storage medium.

Background

In the era of eyeball-making and click-making, the contents of a lot of texts are not coordinated with each other, advertisements may be inserted in paragraphs, and merchants acquire click-making amount, but the time of users is wasted.

However, in the process of reading texts acquired by the existing user, there is no method for filtering advertisements in the texts, and the user has no way to skip the interspersed invalid information and cannot directly find the information which the user wants to see, so that in order to make reading more efficient, irrelevant contents which only show the subject and have organized contents are filtered out, a large amount of time can be saved for the user, and the central meaning which the text wants to express can be directly contacted with the brain of the user, so as to improve the reading experience of the user, and the demands are more and more urgent.

Disclosure of Invention

The embodiment of the invention provides a text display method, a text display device, text display equipment and a storage medium, and aims to filter out inconsistent contents in a text.

To achieve the purpose, an embodiment of the present invention provides a text display method, including:

acquiring a target text;

inputting sentences in the same paragraph in the target text into a pre-trained neural network model in pairs in sequence to obtain the similarity between sentences in the same paragraph;

and displaying the target text according to the similarity between sentences in the same paragraph.

Further, the displaying the target text according to the similarity between sentences in the same paragraph includes:

obtaining the similarity score of each sentence in the same paragraph according to the similarity between sentences in the same paragraph;

and displaying the target text according to the similarity score.

Further, the presenting the target text according to the similarity score includes:

ranking the similarity scores from high to low;

and displaying the target text according to the ranking of the similarity score.

Further, the displaying the target text according to the ranking of the similarity score includes:

acquiring a first sentence with the similarity score ranking larger than a first threshold value and a second sentence with the similarity score ranking smaller than a second threshold value;

the first sentence is presented in a first manner and the second sentence is presented in a second manner.

ranking the similarity scores from high to low;

and eliminating a filtering sentence from the target text for displaying, wherein the ranking of the similarity score of the filtering sentence is larger than a third threshold value or smaller than a fourth threshold value.

acquiring at least two third sentences of which the similarity between sentences in the same paragraph is greater than a fifth threshold;

acquiring the sequence of the third sentence appearing in the same paragraph;

and displaying the target text according to the sequence of the third sentence appearing in the same paragraph.

Further, the neural network model is a twin neural network model.

On one hand, the embodiment of the invention also provides a text display device, which comprises:

the text acquisition module is used for acquiring a target text;

the similarity obtaining module is used for sequentially inputting sentences in the same paragraph in the target text into a pre-trained neural network model in pairs so as to obtain the similarity between the sentences in the same paragraph;

and the text display module is used for displaying the target text according to the similarity between sentences in the same paragraph.

On the other hand, an embodiment of the present invention further provides a computer device, where the computer device includes: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method as provided by any embodiment of the invention.

In yet another aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method provided in any embodiment of the present invention.

The embodiment of the invention obtains the target text; inputting sentences in the same paragraph in the target text into a pre-trained neural network model in pairs in sequence to obtain the similarity between sentences in the same paragraph; the target text is displayed according to the similarity between sentences in the same paragraph, so that the problem that a user cannot skip the invalid contents interspersed in the text is solved, and the effect of filtering the inconsistent contents in the text is realized.

Drawings

Fig. 1 is a schematic flowchart of a text display method according to an embodiment of the present invention;

fig. 2 is a flowchart illustrating a text display method according to a second embodiment of the present invention;

fig. 3 is a schematic structural diagram of a text display apparatus according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are for purposes of illustration and not limitation. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, a first module may be termed a second module, and, similarly, a second module may be termed a first module, without departing from the scope of the present application. The first module and the second module are both modules, but they are not the same module. The terms "first", "second", etc. are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the embodiments of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

Example one

As shown in fig. 1, a first embodiment of the present invention provides a text display method, where the method includes:

and S110, acquiring a target text.

S120, inputting the sentences in the same paragraph in the target text into a pre-trained neural network model in pairs in sequence to obtain the similarity between the sentences in the same paragraph.

In this embodiment, a target text input by a user is first acquired, the text is divided into paragraphs, each paragraph is divided into sentences, and then the sentences in the same paragraph in the target text are sequentially input into a pre-trained neural network model in pairs, so as to obtain the similarity between the sentences in the same paragraph.

Illustratively, the target text comprises a paragraph one, wherein the paragraph one comprises a sentence a, a sentence B and a sentence C, and then the sentence a and the sentence B are taken as a group, the sentence a and the sentence C are taken as a group, and the sentence B and the sentence C are taken as a group, which are input into a pre-trained neural network model once, wherein the neural network model is pre-trained, and after two sentences are input, the similarity of the two sentences can be obtained, so that the similarity of the sentence a and the sentence B, the similarity of the sentence a and the sentence C, and the similarity of the sentence B and the sentence C can be obtained. And then processing paragraphs two, paragraphs three and the like according to the same method until the whole target text is processed.

And S130, displaying the target text according to the similarity between sentences in the same paragraph.

In this embodiment, after the similarity between sentences in the same paragraph is obtained, the target text can be displayed accordingly. Illustratively, if the similarity between sentence a and sentence B is high, the similarity between sentence a and sentence C is low, and the similarity between sentence B and sentence C is low, it can be stated that sentence C is invalid information in the paragraph, and thus sentence C is filtered out and then the target text is displayed.

Preferably, after sentences with low similarity among sentences in the same paragraph are filtered, irrelevant words in the target text can be filtered. Specifically, a Named Entity Recognition technology (NER) is adopted to extract first Entity words in the target text, the first Entity words are input into a Word2vec (Word to vector) neural network model which is trained in advance to obtain first Word vectors of each first Entity Word, a central point of each first Word vector is determined, finally, the first Entity words corresponding to second Word vectors are obtained to serve as filter words of the target text, and the second Word vectors are first Word vectors with the first preset number and the first Word vectors which are farthest from the central point. The first preset number is set by the user, the user sets the first preset number according to the filtering requirement, then the first entity words corresponding to the second word vectors are obtained, and the fact that the second word vectors are far away from the central point indicates that the correlation between the main meanings of the first entity words corresponding to the second word vectors and the target text is low, and therefore the first entity words are used as filtering words of the target text. Therefore, sentences and filter words with low similarity are filtered out, and then the target text is displayed, so that the user can quickly acquire the required information.

Preferably, before the target text is obtained, if a user inputs a plurality of target texts, the target text can be further filtered, and the target text with a text title not in accordance with the text content is filtered. Specifically, a target text and a text title of the target text are obtained, then a first preset number of first keywords are extracted from the target text, a second preset number of second keywords are extracted from the text title, the first keywords are input into a Word2vec (Word to vector) neural network model trained in advance to obtain a first phrase vector, the second keywords are input into a Word2vec neural network model trained in advance to obtain a second phrase vector, then the average vector of the first phrase vector is determined as a first vector, the average vector of the second phrase vector is determined as a second vector, finally a first cosine similarity of the first vector and the second vector is determined, the target text is filtered according to the first cosine similarity, whether the first cosine similarity is smaller than a first threshold value is judged, if the first cosine similarity is smaller than the first threshold value, the target text is filtered, the display of invalid text is avoided. Preferably, before the target text is obtained, a first preset number of first keywords may be extracted from any paragraph of the target text, and the paragraphs of the target text that are not related to the text title are filtered out by the above method.

Example two

As shown in fig. 2, a second embodiment of the present invention provides a text display method, and the second embodiment of the present invention provides a further explanation on the basis of the first embodiment of the present invention, where the method includes:

and S210, acquiring a target text.

S220, inputting the sentences in the same paragraph in the target text into a pre-trained neural network model in pairs in sequence to obtain the similarity between the sentences in the same paragraph.

In this embodiment, the neural Network model is a twin neural Network (Siamese Network) model.

And S230, acquiring the similarity score of each sentence in the same paragraph according to the similarity between sentences in the same paragraph.

S240, sorting the similarity scores from high to low.

S250, acquiring a first sentence with the similarity score ranking larger than a first threshold value and a second sentence with the similarity score ranking smaller than a second threshold value.

S260, displaying the first sentence in a first mode, and displaying the second sentence in a second mode.

In this embodiment, after the similarity between sentences in the same paragraph is obtained, the similarity score of each sentence in the same paragraph can be obtained accordingly. Illustratively, the similarity of the sentence a and the sentence B is high and set to 1 point, the similarity of the sentence a and the sentence C is low and set to 0.5 point, the similarity of the sentence B and the sentence C is low and set to 0.5 point, then the similarity score of the sentence a is an average of 1 point and 0.5 point, 0.75 point, the similarity score of the sentence B is an average of 1 point and 0.5 point, 0.75 point, the similarity score of the sentence C is an average of 0.5 point and 0.5 point, then the similarity scores are sorted from high to low, and a first sentence with the similarity score ranking larger than a first threshold and a second sentence with the similarity score ranking smaller than a second threshold, which may be 2, are obtained, then the first sentence is the sentence a and the sentence B, and the second sentence is the sentence C. Finally, the first sentence is displayed in a first mode, and the second sentence is displayed in a second mode, namely, the sentences A and B are displayed in a highlight and bold mode, and the sentence C is displayed in a low-light and gray mode, so that the user can easily notice the related content of the sentences A and B while neglecting the invalid content of the sentence C.

In an alternative embodiment, steps S250-S260 may be replaced as follows:

s350, sorting the similarity scores from high to low.

And S360, removing the filtered sentences from the target text and then displaying, wherein the ranking of the similarity scores of the filtered sentences is greater than a third threshold value or less than a fourth threshold value.

In this embodiment, after the similarity scores are sorted from high to low, the similarity scores ranked higher than a third threshold or lower than a fourth threshold, that is, sentences corresponding to the similarity scores ranked in the front and the back may also be found, and when there are many sentences of the target text, the sentences may deviate from the central idea or may be sentences without significant meaning, so that the target text is displayed after being filtered.

In an alternative embodiment, steps S230-S260 may be replaced as follows:

and S430, acquiring at least two third sentences of which the similarity between sentences in the same paragraph is greater than a fifth threshold.

And S440, acquiring the sequence of the third sentence appearing in the same paragraph.

S450, displaying the target text according to the sequence of the third sentence appearing in the same paragraph.

In this embodiment, after obtaining the similarity between the sentences in the same paragraph, at least two third sentences whose similarity between the sentences in the same paragraph is greater than a fifth threshold may be obtained, where the third sentences are exemplarily a sentence a and a sentence B, and the similarity between the sentence a and the sentence B is greater than the fifth threshold, which indicates that the similarity between the sentence a and the sentence B is very high, and then the sequence of the third sentences appearing in the same paragraph is obtained, and the target text is displayed according to the sequence of the third sentences appearing in the same paragraph, for example, the sentence a appears first, the sentence a is displayed with a bold and a highlight, and the sentence B with the same meaning can be folded, filtered, displayed with a low brightness or a gray scale, so that the user can filter out the sentences with the same meaning during reading, thereby avoiding invalid reading and saving time.

EXAMPLE III

As shown in fig. 3, a text display apparatus 100 is provided in the third embodiment of the present invention, and the text display apparatus 100 provided in the third embodiment of the present invention can execute the text display method provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method. The text presentation apparatus 100 includes a text acquisition module 200, a similarity acquisition module 300, and a text presentation module 400.

Specifically, the text obtaining module 200 is configured to obtain a target text; the similarity obtaining module 300 is configured to sequentially input every two sentences in the same paragraph in the target text to a pre-trained neural network model to obtain similarity between sentences in the same paragraph; the text display module 400 is configured to display the target text according to the similarity between sentences in the same paragraph.

In this embodiment, the text display module 400 is specifically configured to obtain a similarity score of each sentence in the same paragraph according to a similarity between sentences in the same paragraph; and displaying the target text according to the similarity score. The text presentation module 400 is further configured to rank the similarity scores from high to low; and displaying the target text according to the ranking of the similarity score. The text presentation module 400 is further configured to obtain the first sentence with the similarity score ranking larger than a first threshold and the second sentence with the similarity score ranking smaller than a second threshold; the first sentence is presented in a first manner and the second sentence is presented in a second manner. The text presentation module 400 is further configured to rank the similarity scores from high to low; and eliminating a filtering sentence from the target text for displaying, wherein the ranking of the similarity score of the filtering sentence is larger than a third threshold value or smaller than a fourth threshold value. The text presentation module 400 is further configured to obtain at least two third sentences in the same paragraph, where similarity between the sentences is greater than a fifth threshold; acquiring the sequence of the third sentence appearing in the same paragraph; and displaying the target text according to the sequence of the third sentence appearing in the same paragraph. Preferably, the neural network model is a twin neural network model.

Example four

Fig. 4 is a schematic structural diagram of a computer device 12 according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in FIG. 4 is only one example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.

As shown in FIG. 4, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.

Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, computer device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, implementing the methods provided by the embodiments of the present invention:

acquiring a target text;

EXAMPLE five

The fifth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the methods provided in all the embodiments of the present invention of the present application:

acquiring a target text;

Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. 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 (a non-exhaustive list) of the computer readable storage medium would include the following: 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 context of this document, 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.

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, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A text presentation method, comprising:

acquiring a target text;

2. The method of claim 1, wherein the presenting the target text according to similarity between sentences in the same paragraph comprises:

and displaying the target text according to the similarity score.

3. The method of claim 2, wherein presenting the target text according to the similarity score comprises:

ranking the similarity scores from high to low;

4. The method of claim 3, wherein presenting the target text according to the ranking of the similarity score comprises:

5. The method of claim 2, wherein presenting the target text according to the similarity score comprises:

ranking the similarity scores from high to low;

6. The method of claim 1, wherein the presenting the target text according to similarity between sentences in the same paragraph comprises:

acquiring the sequence of the third sentence appearing in the same paragraph;

7. The method of claim 1, wherein the neural network model is a twin neural network model.

8. A text presentation device, comprising:

the text acquisition module is used for acquiring a target text;

9. A computer device, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.