CN111767371A - Intelligent question and answer method, device, equipment and medium - Google Patents
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Abstract
The embodiment of the invention discloses an intelligent question and answer method, an intelligent question and answer device, electronic equipment and a storage medium, wherein the method comprises the following steps: receiving text information input by a user based on a question and answer page; inputting the text information into a pre-trained intention recognition model to obtain target intentions, and if the text information comprises at least two target intentions, obtaining the at least two target intentions through the intention recognition model respectively; determining whether preset scene configuration exists, if so, recommending behaviors according to the target intention based on the preset scene configuration to obtain response information; otherwise, inputting the target intention into a recommendation model to obtain response information corresponding to the text information; and displaying the response information on a question and answer page. The technical scheme of the embodiment of the invention realizes the multi-intention recognition and answer of the questions proposed by the user and the purpose of giving personalized answers aiming at the same questions of different users.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to an intelligent question answering method, an intelligent question answering device, electronic equipment and a storage medium.
Background
The intelligent question-answering system aims at automatically solving relevant problems proposed by users as good as possible within 24 hours, so that the users can be satisfied and the problems of the users can be solved quickly, and the service pressure of customer service staff is relieved.
In the medical field, the current intelligent question-answering system can only process simple questions, such as single-intention identification and answers, and the answers to the same questions proposed by different users are fixed, and personalized answers are provided for different users without considering personalized features of different users.
Disclosure of Invention
The embodiment of the invention provides an intelligent question-answering method, an intelligent question-answering device, electronic equipment and a storage medium, and aims to realize multi-intention recognition and answering of questions posed by users and give personalized answers to the same questions of different users.
In a first aspect, an embodiment of the present invention provides an intelligent question answering method, including:
receiving text information input by a user based on a question and answer page;
inputting the text information into a pre-trained intention recognition model to obtain target intentions corresponding to the text information, and if the text information comprises at least two target intentions, obtaining the at least two target intentions through the intention recognition model respectively;
determining whether preset scene configuration exists, if so, recommending behaviors according to the target intention based on the preset scene configuration to obtain response information;
if the preset scene configuration does not exist, inputting the target intention into a recommendation model, and performing behavior recommendation through the recommendation model to obtain response information corresponding to the text information;
and displaying the response information on a question and answer page to provide for the user.
In a second aspect, an embodiment of the present invention further provides an intelligent question answering device, where the device includes:
the receiving module is used for receiving text information input by a user based on a question and answer page;
the recognition module is used for inputting the text information into a pre-trained intention recognition model to obtain a target intention corresponding to the text information, and if the text information comprises at least two target intentions, the at least two target intentions can be respectively obtained through the intention recognition model;
the first recommendation module is used for determining whether preset scene configuration exists or not, and if the preset scene configuration exists, behavior recommendation is performed according to the target intention based on the preset scene configuration to obtain response information;
the second recommendation module is used for inputting the target intention into a recommendation model if no preset scene configuration exists, recommending behaviors through the recommendation model and acquiring response information corresponding to the text information;
and the response module is used for displaying the response information on a question and answer page so as to provide the response information for the user.
In a third aspect, an embodiment of the present invention further provides an apparatus, where the apparatus includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the intelligent question and answer method according to any one of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the intelligent question answering method according to any one of the embodiments of the present invention.
According to the intelligent question and answer method provided by the embodiment of the invention, text information input by a user based on a question and answer page is received; inputting the text information into a pre-trained intention recognition model to obtain target intentions corresponding to the text information, and if the text information comprises at least two target intentions, obtaining the at least two target intentions through the intention recognition model respectively; determining whether preset scene configuration exists, if so, recommending behaviors according to the target intention based on the preset scene configuration to obtain response information; if the preset scene configuration does not exist, inputting the target intention into a recommendation model, and performing behavior recommendation through the recommendation model to obtain response information corresponding to the text information; the response information is displayed on a question and answer page to provide the technical means for the user, so that multi-intention identification and answer of the questions proposed by the user are realized, the identification precision of the user intention is improved, and the answer precision of the questions proposed by the user is improved.
Drawings
The above and other features, advantages and aspects of various embodiments of the present invention will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
Fig. 1 is a schematic flow chart of an intelligent question answering method according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a target intent recognition method according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of another intelligent question answering method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an intelligent question answering device according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and the embodiments of the present invention are illustrative only and are not intended to limit the scope of the present invention.
It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present invention are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in the present invention are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
Example one
Fig. 1 is a schematic flow chart of an intelligent question answering method according to an embodiment of the present invention. The method is suitable for the intelligent question answering system. The method may be performed by a smart question-answering device, which may be implemented in software and/or hardware.
As shown in fig. 1, the intelligent question answering method provided by the embodiment of the present invention includes the following steps:
and step 110, receiving text information input by a user based on the question and answer page.
Wherein, the text information is different according to different application scenarios, for example, the intelligent question-answering system is applied in the medical field, and the text information is some medical related questions, for example, specifically, "what food is suitable for diabetic? "," i are now headache, are there hospitals nearby? "and the like.
Wherein the intent recognition model comprises a BERT model layer and a fully connected layer;
the input of the BERT model layer is the text information, the output of the BERT model layer is the input of the full connection layer, the output value of each node of the full connection layer is the probability of each preset intention to which the text information belongs, and the preset intention corresponding to the probability reaching a set value is the target intention.
The preset intents include at least two of the following: customer service intent, content intent, hospital intent, registration intent, department intent, and doctor intent.
Correspondingly, referring to a schematic flow diagram of the target intention recognition shown in fig. 2, the method specifically includes: specifically, the input text is converted into an input format (for example, a position vector + a word vector + a segment vector) required by the model and then input into the model, and the output (CLS label) of the BERT model layer is used as the input of a full connection layer to obtain the probability of each preset intention to which the input text belongs.
For example, the text information is: "do my headache seriously, what doctors can register nearby? Inputting the text information into the intention recognition model, and finally outputting the probability that the text information belongs to each preset intention by each node of the full-connection layer of the intention recognition model, for example, the probability corresponding to the doctor intention is 0.75, the probability corresponding to the hospital intention is 0.2, the probability corresponding to the department intention is 0.1, the probability corresponding to the registration intention is 0.8, the probability corresponding to the customer service intention is 0.01, and the probability corresponding to the content intention is 0.02, intercepting the intention that the probability does not reach the set value through a preset evaluation threshold value, and finally obtaining two target intentions of the doctor intention and the registration intention, namely the text information is: "do my headache seriously, what doctors can register nearby? The number of the target intentions is two, namely the doctor intention and the registration intention, so that the user intention can be accurately and comprehensively identified.
The BERT model layer is pre-trained based on training data, the format of which is: the text information and the intention labels contained in the text information, the number of the intention labels comprises: at least two, it can be understood that the number of the intention labels also includes one, so that the BERT model layer has the function of intention recognition of the user-input simple question sentence. The BERT model layer is trained in advance by utilizing training data comprising at least two intention labels, so that the trained BERT model layer has the function of comprehensively identifying the intention of a user, reference basis is provided for accurate response of the problem input by the user, and the purpose of accurate response of the problem input by the user is finally realized.
The BERT model is a new language representation model represented by a transform bidirectional encoder and aims to pre-train a deep bidirectional representation by jointly adjusting the context in all layers, and the characteristic extraction capability of the BERT model is very outstanding.
For example, the intelligent question-answering device provided by the embodiment of the invention is applied to a hospital A reception system, the hospital A cooperates with a third-party application platform, deploys the reception system to the third-party application platform, and when a user enters the reception system of the hospital A based on the third-party application platform, the user inputs "how do my headache be serious, what doctors can register nearby? When "time, first, after obtaining the target intentions corresponding to the problem as" doctor intention "and" registration intention "through step 120, the doctor in charge of taking a visit to the headache patient in the first hospital configured in the first hospital carries out behavior recommendation, that is, only the doctor in the first hospital is recommended, regardless of whether the first hospital is in the vicinity of the current user.
For example, if there is a preset scene configuration, performing behavior recommendation according to the target intention based on the preset scene configuration, and obtaining response information includes:
and when the target intention is obtained, executing behavior recommendation corresponding to the target intention and pre-stored in a preset scene configuration, and obtaining response information corresponding to the target intention.
And 140, if no preset scene configuration exists, inputting the target intention into a recommendation model, and performing behavior recommendation through the recommendation model to obtain response information corresponding to the text information.
Wherein, the recommendation model comprises a 2-layer LSTM (Long Short-Term Memory, Long Short-Term Memory network) model, and the input of the recommendation model further comprises: historical behavior information of the user, historical text information input by the user before the input time of the text information and associated information of the historical text information;
the association information of the historical text information comprises at least one of the following: disease symptom entity words included in the historical text information, target intentions corresponding to the historical text information and response information corresponding to the target intentions.
The historical behavior information of the user can refer to historical behavior data generated by the user on an application platform of the intelligent question-answering device, or historical behavior data generated by the user on any third-party application platform, and the historical behavior information of the user can be acquired as long as authorization of a related platform is acquired. For example, the historical behavior information of the user is related information for the user to see a doctor in each hospital, such as a department with a hung number, a doctor and the like, and even historical examination report data of the user can be included, so that a portrait label of the user is determined based on the historical behavior information of the user, a reference is provided for behavior recommendation, an obtained recommendation result is more consistent with the internal expectation of the user, and personalized recommendation is realized, and fixed response information is not provided for the same problem of all users.
Further, in order to accurately perform behavior recommendation aiming at the target intention, the input of the recommendation model not only comprises the relevant information of the user in the current wheel conversation, but also comprises the relevant information of the user in the previous or several wheel conversations. The relevant information of the user in the current round of conversation comprises text information input by the user in the current round and a target intention corresponding to the text information; the relevant information of the user in each dialog in the last rounds comprises the text information input by the user, the target intention corresponding to the text information and the response information fed back aiming at the text information, so that the recommendation model can accurately recommend to the user in relation to the context.
Further, the input of the recommendation model further comprises entity words representing disease symptoms in each round of input information.
Illustratively, if the text information includes at least two target intentions, the inputting the target intentions into a recommendation model, performing behavior recommendation through the recommendation model, and obtaining response information corresponding to the text information includes:
behavior recommendation is carried out on the basis of each target intention through the recommendation model, and behavior recommendation results corresponding to each target intention are obtained;
performing data splicing on behavior recommendation results corresponding to different target intentions according to a set format to obtain response information corresponding to the text information;
the behavior recommendation includes at least one of: customer service recommendations, content recommendations, hospital recommendations, registration recommendations, department recommendations, and doctor recommendations.
And 150, displaying the response information on a question and answer page to provide for the user.
According to the technical scheme of the embodiment of the invention, text information input by a user based on a question and answer page is received; inputting the text information into a pre-trained intention recognition model to obtain target intentions corresponding to the text information, and if the text information comprises at least two target intentions, obtaining the at least two target intentions through the intention recognition model respectively; determining whether preset scene configuration exists, if so, recommending behaviors according to the target intention based on the preset scene configuration to obtain response information; if the preset scene configuration does not exist, inputting the target intention into a recommendation model, and performing behavior recommendation through the recommendation model to obtain response information corresponding to the text information; the response information is displayed on a question and answer page to provide the technical means for the user, so that multi-intention identification and answer of the questions proposed by the user are realized, the identification precision of the user intention is improved, and the answer precision of the questions proposed by the user is improved.
On the basis of the above technical solution, referring to a flow diagram of another intelligent question answering method shown in fig. 3, the method specifically includes: and identifying the intention of the input text to obtain a target intention, acquiring a corresponding recommendation behavior according to the target intention, acquiring a recommendation result, and assembling and displaying the recommendation result.
Example two
Fig. 4 is an intelligent question answering device provided in the second embodiment of the present invention, which includes: a receiving module 410, an identifying module 420, a first recommending module 430, a second recommending module 440 and a responding module 450;
the receiving module 410 is configured to receive text information input by a user based on a question and answer page; the recognition module 420 is configured to input the text information into a pre-trained intent recognition model to obtain target intentions corresponding to the text information, and if the text information includes at least two target intentions, the at least two target intentions may be obtained through the intent recognition model respectively; the first recommending module 430 is configured to determine whether a preset scene configuration exists, and if the preset scene configuration exists, perform behavior recommendation according to the target intention based on the preset scene configuration to obtain response information; the second recommending module 440 is configured to, if there is no preset scene configuration, input the target intention into a recommending model, perform behavior recommendation through the recommending model, and obtain response information corresponding to the text information; the response module 450 is configured to display the response information on a question and answer page to provide to the user.
On the basis of the technical scheme, the intention recognition model comprises a BERT model layer and a full connection layer;
the input of the BERT model layer is the text information, the output of the BERT model layer is the input of the full connection layer, the output value of each node of the full connection layer is the probability of each preset intention to which the text information belongs, and the preset intention corresponding to the probability reaching a set value is the target intention.
On the basis of the technical scheme, the preset intention comprises at least two types as follows: customer service intent, content intent, hospital intent, registration intent, department intent, and doctor intent.
On the basis of the technical scheme, the BERT model layer is trained in advance based on training data, and the format of the training data is as follows: the text information and the intention labels contained in the text information, the number of the intention labels comprises: at least two.
On the basis of the above technical solutions, if there is a preset scene configuration, the first recommendation module 430 is specifically configured to: and when the target intention is obtained, executing behavior recommendation corresponding to the target intention and pre-stored in a preset scene configuration, and obtaining response information corresponding to the target intention.
On the basis of the above technical solutions, the recommendation model includes a 2-layer long-short term memory network LSTM model, and the input of the recommendation model further includes: historical behavior information of the user, historical text information input by the user before the input time of the text information and associated information of the historical text information;
the association information of the historical text information comprises at least one of the following: disease symptom entity words included in the historical text information, target intentions corresponding to the historical text information and response information corresponding to the target intentions.
On the basis of the above technical solutions, if the text information includes at least two target intentions, the second recommending module 440 includes:
the recommendation unit is used for performing behavior recommendation respectively based on each target intention through the recommendation model to obtain behavior recommendation results respectively corresponding to each target intention;
the splicing unit is used for splicing the data of the behavior recommendation results corresponding to different target intentions according to a set format to obtain response information corresponding to the text information;
the behavior recommendation includes at least one of: customer service recommendations, content recommendations, hospital recommendations, registration recommendations, department recommendations, and doctor recommendations.
According to the technical scheme of the embodiment of the invention, text information input by a user based on a question and answer page is received; inputting the text information into a pre-trained intention recognition model to obtain target intentions corresponding to the text information, and if the text information comprises at least two target intentions, obtaining the at least two target intentions through the intention recognition model respectively; determining whether preset scene configuration exists, if so, recommending behaviors according to the target intention based on the preset scene configuration to obtain response information; if the preset scene configuration does not exist, inputting the target intention into a recommendation model, and performing behavior recommendation through the recommendation model to obtain response information corresponding to the text information; the response information is displayed on a question and answer page to provide the technical means for the user, so that multi-intention identification and answer of the questions proposed by the user are realized, the identification precision of the user intention is improved, and the answer precision of the questions proposed by the user is improved.
The intelligent question answering device provided by the embodiment of the invention can execute the intelligent question answering method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.
EXAMPLE III
Referring now to fig. 5, a schematic diagram of an electronic device (e.g., the terminal device or server of fig. 5) 400 suitable for implementing embodiments of the present invention is shown. The terminal device in the embodiments of the present invention may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the electronic device 400 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 406 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 406 including, for example, magnetic tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 5 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, an embodiment of the invention includes a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 409, or from the storage means 406, or from the ROM 402. The computer program performs the above-described functions defined in the methods of embodiments of the invention when executed by the processing apparatus 401.
The terminal provided by the embodiment of the invention and the intelligent question answering method provided by the embodiment belong to the same inventive concept, technical details which are not described in detail in the embodiment of the invention can be referred to the embodiment, and the embodiment of the invention and the embodiment have the same beneficial effects.
Example four
An embodiment of the present invention provides a computer storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the intelligent question answering method provided in the foregoing embodiment.
It should be noted that the computer readable medium of the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either 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: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:
receiving text information input by a user based on a question and answer page;
inputting the text information into a pre-trained intention recognition model to obtain target intentions corresponding to the text information, and if the text information comprises at least two target intentions, obtaining the at least two target intentions through the intention recognition model respectively;
determining whether preset scene configuration exists, if so, recommending behaviors according to the target intention based on the preset scene configuration to obtain response information;
if the preset scene configuration does not exist, inputting the target intention into a recommendation model, and performing behavior recommendation through the recommendation model to obtain response information corresponding to the text information;
and displaying the response information on a question and answer page to provide for the user.
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 but not limited to an object oriented programming language such as Java, Smalltalk, C + +, 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).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software or hardware. Where the name of a cell does not in some cases constitute a limitation on the cell itself, for example, an editable content display cell may also be described as an "editing cell".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
The foregoing description is only exemplary of the preferred embodiments of the invention and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents is encompassed without departing from the spirit of the disclosure. For example, the above features and (but not limited to) features having similar functions disclosed in the present invention are mutually replaced to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the invention. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. An intelligent question answering method is characterized by comprising the following steps:
receiving text information input by a user based on a question and answer page;
inputting the text information into a pre-trained intention recognition model to obtain target intentions corresponding to the text information, and if the text information comprises at least two target intentions, obtaining the at least two target intentions through the intention recognition model respectively;
determining whether preset scene configuration exists, if so, recommending behaviors according to the target intention based on the preset scene configuration to obtain response information;
if the preset scene configuration does not exist, inputting the target intention into a recommendation model, and performing behavior recommendation through the recommendation model to obtain response information corresponding to the text information;
and displaying the response information on a question and answer page to provide for the user.
2. The method of claim 1, wherein the intent recognition model comprises a BERT model layer and a fully connected layer;
the input of the BERT model layer is the text information, the output of the BERT model layer is the input of the full connection layer, the output value of each node of the full connection layer is the probability of each preset intention to which the text information belongs, and the preset intention corresponding to the probability reaching a set value is the target intention.
3. The method of claim 2, wherein the preset intents include at least two of: customer service intent, content intent, hospital intent, registration intent, department intent, and doctor intent.
4. The method of claim 2, wherein the BERT model layer is pre-trained based on training data having a format of: the text information and the intention labels contained in the text information, the number of the intention labels comprises: at least two.
5. The method according to any one of claims 1 to 4, wherein if a preset scene configuration exists, performing behavior recommendation according to the target intention based on the preset scene configuration, and obtaining response information includes:
and when the target intention is obtained, executing behavior recommendation corresponding to the target intention and pre-stored in a preset scene configuration, and obtaining response information corresponding to the target intention.
6. The method of any of claims 1-4, wherein the recommendation model comprises a layer 2 long short term memory network (LSTM) model, and wherein the input to the recommendation model further comprises: historical behavior information of the user, historical text information input by the user before the input time of the text information and associated information of the historical text information;
the association information of the historical text information comprises at least one of the following: disease symptom entity words included in the historical text information, target intentions corresponding to the historical text information and response information corresponding to the target intentions.
7. The method according to any one of claims 1 to 4, wherein if the text information includes at least two target intentions, the inputting the target intentions into a recommendation model, performing behavior recommendation through the recommendation model, and obtaining response information corresponding to the text information includes:
behavior recommendation is carried out on the basis of each target intention through the recommendation model, and behavior recommendation results corresponding to each target intention are obtained;
performing data splicing on behavior recommendation results corresponding to different target intentions according to a set format to obtain response information corresponding to the text information;
the behavior recommendation includes at least one of: customer service recommendations, content recommendations, hospital recommendations, registration recommendations, department recommendations, and doctor recommendations.
8. An intelligent question answering device, comprising:
the receiving module is used for receiving text information input by a user based on a question and answer page;
the recognition module is used for inputting the text information into a pre-trained intention recognition model to obtain a target intention corresponding to the text information, and if the text information comprises at least two target intentions, the at least two target intentions can be respectively obtained through the intention recognition model;
the first recommendation module is used for determining whether preset scene configuration exists or not, and if the preset scene configuration exists, behavior recommendation is performed according to the target intention based on the preset scene configuration to obtain response information;
the second recommendation module is used for inputting the target intention into a recommendation model if no preset scene configuration exists, recommending behaviors through the recommendation model and acquiring response information corresponding to the text information;
and the response module is used for displaying the response information on a question and answer page so as to provide the response information for the user.
9. An electronic device, characterized in that the electronic device comprises:
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 intelligent question-answering method of any one of claims 1-7.
10. A storage medium containing computer-executable instructions for performing the intelligent question answering method according to any one of claims 1-7 when executed by a computer processor.
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