CN113779964A - Statement segmentation method and device - Google Patents
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Abstract
The invention discloses a sentence segmentation method and a sentence segmentation device. Wherein, the method comprises the following steps: acquiring first statement data; classifying according to the first statement data to obtain statement data to be segmented; inputting the sentence data to be segmented into a sentence segmentation model to obtain a segmentation result; and displaying the segmentation result. The sentence segmentation method solves the technical problems that in the prior art, the sentence segmentation method is only segmented through the specified segmentation rules and the segmentation algorithm, and the sentence segmentation rules cannot be adjusted by referring to the historical data of the sentence application scene or the factor change condition of the sentence application scene, so that the sentence segmentation accuracy and efficiency are reduced.
Description
Technical Field
The invention relates to the field of statement analysis, in particular to a statement segmentation method and a statement segmentation device.
Background
Along with the continuous development of intelligent science and technology, people use intelligent equipment more and more among life, work, the study, use intelligent science and technology means, improved the quality of people's life, increased the efficiency of people's study and work.
Currently, in the sentence analyzing and dividing process, a sentence is generally operated by using a fixed sentence division or a sentence analyzing rule, but the conventional sentence dividing method divides the sentence by only using a predetermined division rule and a division algorithm, and cannot adjust the sentence dividing rule by referring to the history data of the sentence application scenario or the factor change condition of the sentence application scenario, so that the precision degree and the efficiency of the sentence division are reduced.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a sentence segmentation method and a sentence segmentation device, which at least solve the technical problems that in the prior art, the sentence segmentation method is only segmented by a specified segmentation rule and a segmentation algorithm, and the sentence segmentation rule cannot be adjusted by referring to historical data of a sentence application scene or factor change conditions of the sentence application scene, so that the sentence segmentation accuracy and efficiency are reduced.
According to an aspect of an embodiment of the present invention, there is provided a sentence segmentation method, including: acquiring first statement data; classifying according to the first statement data to obtain statement data to be segmented; inputting the sentence data to be segmented into a sentence segmentation model to obtain a segmentation result; and displaying the segmentation result.
Optionally, the classifying according to the first sentence data to obtain the sentence data to be segmented includes: acquiring a preset classification matrix; and classifying the first statement data through the preset classification matrix to obtain the statement data to be segmented.
Optionally, the segmentation result includes: content and rules.
Optionally, before displaying the segmentation result, the method further includes: and checking the segmentation result and the first statement data.
According to another aspect of the embodiments of the present invention, there is also provided a sentence segmentation apparatus, including: the acquisition module is used for acquiring first statement data; the classification module is used for classifying according to the first statement data to obtain statement data to be segmented; the segmentation module is used for inputting the statement data to be segmented into a statement segmentation model to obtain a segmentation result; and the display module is used for displaying the segmentation result.
Optionally, the classification module includes: the device comprises an acquisition unit, a classification unit and a classification unit, wherein the acquisition unit is used for acquiring a preset classification matrix; and the classification unit is used for classifying the first statement data through the preset classification matrix to obtain the statement data to be segmented.
Optionally, the segmentation result includes: content and rules.
Optionally, the apparatus further comprises: and the checking module is used for checking the segmentation result and the first statement data.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, which includes a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute a statement segmentation method when running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory has stored therein computer readable instructions, and the processor is configured to execute the computer readable instructions, wherein the computer readable instructions when executed perform a sentence segmentation method.
In the embodiment of the invention, the first statement data is obtained; classifying according to the first statement data to obtain statement data to be segmented; inputting the sentence data to be segmented into a sentence segmentation model to obtain a segmentation result; the method for displaying the segmentation result solves the technical problem that the sentence segmentation method in the prior art only performs segmentation through the specified segmentation rule and the segmentation algorithm, and cannot adjust the sentence segmentation rule by referring to the historical data of the sentence application scene or the factor change condition of the sentence application scene, so that the sentence segmentation accuracy and efficiency are reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a sentence segmentation method according to an embodiment of the invention;
fig. 2 is a block diagram of a sentence division apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a method embodiment of a sentence segmentation method, it is noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Example one
Fig. 1 is a flowchart of a sentence segmentation method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, first statement data is acquired.
Specifically, in order to perform sentence division, first sentence data needs to be acquired, where the first sentence data is acquired by the acquisition device and is original sentence information that needs to be divided, and the divided sentence data can be acquired through subsequent division processing.
It should be noted that, when performing the sentence segmentation operation described in the embodiment of the present invention, an original sentence resource is required to be obtained, the original sentence resource may be obtained by acquiring voice information of a user through a sound acquisition device, such as a microphone, and converting the voice information into voice data for storage and utilization, the original sentence resource may also be obtained by taking or scanning a sentence character through a camera device of an image scanning device, so as to identify the acquired sentence image through an image identification function, and store and utilize the identified sentence data, and furthermore, the original sentence resource may also be obtained by performing sentence input through an input device, such as a keyboard, a touch panel, and the like, and storing and utilizing the input sentence data, so as to perform a subsequent sentence segmentation operation.
And step S104, classifying according to the first statement data to obtain statement data to be segmented.
Specifically, in order to perform the sentence segmentation operation quickly, efficiently and accurately after the first sentence data is acquired, the first sentence data needs to be classified, and the sentence data to be segmented is generated according to the sentence data of different types, because the sentences of different types and different scenes have different segmentation rules and different segmentation models, the first sentence data needs to be classified when the sentence to be segmented is generated, so that the segmentation operation is performed according to the classification result and the classification label, and the efficiency of the whole segmentation processing can be increased.
Optionally, the classifying according to the first sentence data to obtain the sentence data to be segmented includes: acquiring a preset classification matrix; and classifying the first statement data through the preset classification matrix to obtain the statement data to be segmented.
Specifically, before segmenting a sentence, the first sentence data acquired in the above embodiment needs to be classified, and the classified sentence data is output as the sentence data to be segmented, so that the sentence data to be segmented is segmented by the neural network model in the following step.
And S106, inputting the statement data to be segmented into a statement segmentation model to obtain a segmentation result.
Optionally, the segmentation result includes: content and rules.
Specifically, after the segmentation of the segmentation statement by the neural network model, a segmentation result is obtained, and the segmentation result is used as a final segmentation result of statement data to be subsequently displayed and calibrated, wherein the segmentation result includes: content and rules.
It should be noted that the sentence segmentation model may be obtained by training through a DNN deep neural network model in combination with different sentence classification scenarios, and a perfect sentence segmentation model is generated by using the sentence segmentation history data and the input and output rules set by the user, and is used to input the sentence data to be segmented to obtain a final segmentation result, where DNN deep learning is a general term of a type of pattern analysis method, and in terms of specific research contents, mainly relates to three types of methods: (1) a neural network system based on convolution operations, i.e. a Convolutional Neural Network (CNN). (2) self-Coding neural networks based on multi-layer neurons include both self-Coding (Auto encoder) and Sparse Coding (Sparse Coding) which has received much attention in recent years. (3) And pre-training in a multilayer self-coding neural network mode, and further optimizing a Deep Belief Network (DBN) of the neural network weight by combining the identification information. Through multi-layer processing, after the initial low-layer feature representation is gradually converted into the high-layer feature representation, the complex learning tasks such as classification can be completed by using a simple model. Thus, deep learning can be understood as "feature learning" or "meaning learning".
And step S108, displaying the segmentation result.
Optionally, before displaying the segmentation result, the method further includes: and checking the segmentation result and the first statement data.
Specifically, before the segmentation result is displayed, a verification operation needs to be performed according to the segmentation result and the first sentence data which is not segmented, and the verification result is fed back, so that the accuracy of the whole sentence segmentation process is increased, and the sending of errors is reduced.
Through the embodiment, the technical problem that the sentence division method in the prior art only divides the sentences through the specified division rules and the division algorithm, and cannot adjust the sentence division rules by referring to the historical data of the sentence application scene or the factor change condition of the sentence application scene, so that the sentence division accuracy and efficiency are reduced is solved.
Example two
Fig. 2 is a block diagram of a sentence segmentation apparatus according to an embodiment of the present invention, as shown in fig. 2, the apparatus includes:
an obtaining module 20, configured to obtain the first statement data.
Specifically, in order to perform sentence division, first sentence data needs to be acquired, where the first sentence data is acquired by the acquisition device and is original sentence information that needs to be divided, and the divided sentence data can be acquired through subsequent division processing.
It should be noted that, when performing the sentence segmentation operation described in the embodiment of the present invention, an original sentence resource is required to be obtained, the original sentence resource may be obtained by acquiring voice information of a user through a sound acquisition device, such as a microphone, and converting the voice information into voice data for storage and utilization, the original sentence resource may also be obtained by taking or scanning a sentence character through a camera device of an image scanning device, so as to identify the acquired sentence image through an image identification function, and store and utilize the identified sentence data, and furthermore, the original sentence resource may also be obtained by performing sentence input through an input device, such as a keyboard, a touch panel, and the like, and storing and utilizing the input sentence data, so as to perform a subsequent sentence segmentation operation.
And the classification module 22 is configured to classify the first sentence data to obtain sentence data to be segmented.
Specifically, in order to perform the sentence segmentation operation quickly, efficiently and accurately after the first sentence data is acquired, the first sentence data needs to be classified, and the sentence data to be segmented is generated according to the sentence data of different types, because the sentences of different types and different scenes have different segmentation rules and different segmentation models, the first sentence data needs to be classified when the sentence to be segmented is generated, so that the segmentation operation is performed according to the classification result and the classification label, and the efficiency of the whole segmentation processing can be increased.
Optionally, the classification module includes: the device comprises an acquisition unit, a classification unit and a classification unit, wherein the acquisition unit is used for acquiring a preset classification matrix; and the classification unit is used for classifying the first statement data through the preset classification matrix to obtain the statement data to be segmented.
Specifically, before segmenting a sentence, the first sentence data acquired in the above embodiment needs to be classified, and the classified sentence data is output as the sentence data to be segmented, so that the sentence data to be segmented is segmented by the neural network model in the following step.
And the segmentation module 24 is configured to input the statement data to be segmented to the statement segmentation model to obtain a segmentation result.
Optionally, the segmentation result includes: content and rules.
Specifically, after the segmentation of the segmentation statement by the neural network model, a segmentation result is obtained, and the segmentation result is used as a final segmentation result of statement data to be subsequently displayed and calibrated, wherein the segmentation result includes: content and rules.
It should be noted that the sentence segmentation model may be obtained by training through a DNN deep neural network model in combination with different sentence classification scenarios, and a perfect sentence segmentation model is generated by using the sentence segmentation history data and the input and output rules set by the user, and is used to input the sentence data to be segmented to obtain a final segmentation result, where DNN deep learning is a general term of a type of pattern analysis method, and in terms of specific research contents, mainly relates to three types of methods: (1) a neural network system based on convolution operations, i.e. a Convolutional Neural Network (CNN). (2) self-Coding neural networks based on multi-layer neurons include both self-Coding (Auto encoder) and Sparse Coding (Sparse Coding) which has received much attention in recent years. (3) And pre-training in a multilayer self-coding neural network mode, and further optimizing a Deep Belief Network (DBN) of the neural network weight by combining the identification information. Through multi-layer processing, after the initial low-layer feature representation is gradually converted into the high-layer feature representation, the complex learning tasks such as classification can be completed by using a simple model. Thus, deep learning can be understood as "feature learning" or "meaning learning".
And a display module 26, configured to display the segmentation result.
Optionally, the apparatus further comprises: and the checking module is used for checking the segmentation result and the first statement data.
Specifically, before the segmentation result is displayed, a verification operation needs to be performed according to the segmentation result and the first sentence data which is not segmented, and the verification result is fed back, so that the accuracy of the whole sentence segmentation process is increased, and the sending of errors is reduced.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, which includes a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute a statement segmentation method when running.
Specifically, the method comprises the following steps: acquiring first statement data; classifying according to the first statement data to obtain statement data to be segmented; inputting the sentence data to be segmented into a sentence segmentation model to obtain a segmentation result; and displaying the segmentation result.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory has stored therein computer readable instructions, and the processor is configured to execute the computer readable instructions, wherein the computer readable instructions when executed perform a sentence segmentation method.
Specifically, the method comprises the following steps: acquiring first statement data; classifying according to the first statement data to obtain statement data to be segmented; inputting the sentence data to be segmented into a sentence segmentation model to obtain a segmentation result; and displaying the segmentation result.
Through the embodiment, the technical problem that the sentence division method in the prior art only divides the sentences through the specified division rules and the division algorithm, and cannot adjust the sentence division rules by referring to the historical data of the sentence application scene or the factor change condition of the sentence application scene, so that the sentence division accuracy and efficiency are reduced is solved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A sentence segmentation method, comprising:
acquiring first statement data;
classifying according to the first statement data to obtain statement data to be segmented;
inputting the sentence data to be segmented into a sentence segmentation model to obtain a segmentation result;
and displaying the segmentation result.
2. The method of claim 1, wherein the classifying according to the first sentence data to obtain the sentence data to be segmented comprises:
acquiring a preset classification matrix;
and classifying the first statement data through the preset classification matrix to obtain the statement data to be segmented.
3. The method of claim 1, wherein the segmentation result comprises: content and rules.
4. The method of claim 1, wherein prior to said presenting the segmentation results, the method further comprises:
and checking the segmentation result and the first statement data.
5. A sentence segmentation apparatus, comprising:
the acquisition module is used for acquiring first statement data;
the classification module is used for classifying according to the first statement data to obtain statement data to be segmented;
the segmentation module is used for inputting the statement data to be segmented into a statement segmentation model to obtain a segmentation result;
and the display module is used for displaying the segmentation result.
6. The apparatus of claim 5, wherein the classification module comprises:
the device comprises an acquisition unit, a classification unit and a classification unit, wherein the acquisition unit is used for acquiring a preset classification matrix;
and the classification unit is used for classifying the first statement data through the preset classification matrix to obtain the statement data to be segmented.
7. The apparatus of claim 5, wherein the segmentation result comprises: content and rules.
8. The apparatus of claim 5, further comprising:
and the checking module is used for checking the segmentation result and the first statement data.
9. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 4.
10. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform the method of any one of claims 1 to 4.
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