CN113806475A - Information reply method and device, electronic equipment and storage medium - Google Patents

Information reply method and device, electronic equipment and storage medium Download PDF

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CN113806475A
CN113806475A CN202110419128.1A CN202110419128A CN113806475A CN 113806475 A CN113806475 A CN 113806475A CN 202110419128 A CN202110419128 A CN 202110419128A CN 113806475 A CN113806475 A CN 113806475A
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named entity
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CN113806475B (en
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闫慧丽
顾松庠
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Jingdong Technology Holding Co Ltd
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Abstract

The application provides an information reply method, an information reply device, electronic equipment and a storage medium, wherein the method comprises the following steps: the method comprises the steps of obtaining a problem to be replied, comparing the similarity between the problem to be replied and each preset problem with a preset similarity threshold, carrying out named entity recognition on the problem to be replied under the condition that the similarity between the problem to be replied and each preset problem is smaller than the preset similarity threshold so as to obtain a named entity recognition result of the problem to be replied, determining reply information of the problem to be replied according to the named entity recognition result, and outputting the reply information. Therefore, the similarity between the received problem to be replied and each preset problem is smaller than the preset similarity threshold, named entity recognition is carried out on the problem to be replied, reply information of the problem to be replied is determined according to the recognition result of the named entity of the problem to be replied, reply of the problem to be replied is achieved, and human-computer interaction experience is improved.

Description

Information reply method and device, electronic equipment and storage medium
Technical Field
The present application relates to the field of computers, and in particular, to an information replying method and apparatus, an electronic device, and a storage medium.
Background
In the related technology, in the current intelligent customer service system of the vertical domain chat robot, after a user inputs problem information into the intelligent customer service system in the process of interacting with the intelligent customer service system, when the situation that the problem close to the technical problem does not exist in a knowledge base is determined, the intelligent customer service system generally directly outputs clear statements. The interaction mode of only giving a clear statement is too simple, and the human-computer interaction experience of the user is influenced.
Disclosure of Invention
The application provides an information reply method, an information reply device, electronic equipment and a storage medium.
An embodiment of one aspect of the present application provides an information replying method, including: acquiring a question to be replied; under the condition that the similarity between the problem to be replied and each preset problem is smaller than a preset similarity threshold, conducting named entity recognition on the problem to be replied to obtain a named entity recognition result of the problem to be replied; determining reply information of the question to be replied according to the named entity recognition result; and outputting the reply information.
In an embodiment of the present application, the performing named entity recognition on the question to be replied to obtain a named entity recognition result of the question to be replied includes: and inputting the problem to be replied to a pre-trained named entity recognition model to obtain a named entity recognition result of the problem to be replied.
In an embodiment of the present application, the named entity recognition model includes a semantic representation layer, a conditional random field layer, and a full connection layer, which are connected in sequence, and the problem to be replied is input to a named entity recognition model trained in advance to obtain a named entity recognition result of the problem to be replied, including: inputting the question to be replied into the semantic representation layer to obtain semantic representation characteristics of the question to be replied; inputting the semantic representation characteristics into the conditional random field layer to obtain the probability of each participle in the problem to be replied corresponding to the named entity category; and inputting the probability of the named entity category corresponding to each participle into a full connection layer to obtain a named entity recognition result corresponding to the problem to be replied.
In an embodiment of the application, before determining reply information of the question to be replied according to the named entity recognition result, the method includes: determining the category number of the named entity categories according to the named entity identification result; and under the condition that the number of the categories is one, generating corresponding question-answering sentence information according to the text information corresponding to the named entity categories, and taking the question-answering sentence information as the reply information of the question to be replied.
In one embodiment of the present application, the method further comprises: under the condition that the number of the categories is multiple, disassembling the problem to be replied to disassemble the problem to be replied into a plurality of sentences; determining a named entity type of each statement, and generating question-back statement information corresponding to the statement according to text information corresponding to the named entity type; and generating reply information of the question to be replied according to the question-asking statement information corresponding to each statement.
In an embodiment of the application, the generating corresponding question-back sentence information according to the text information corresponding to the named entity category includes: judging whether the number of the operation words is multiple or not under the condition that the named entity type is the operation words; and if the number of the operation words is multiple, generating corresponding question-back sentence information according to the text information corresponding to each operation word.
In one embodiment of the present application, the method further comprises: if the number of the operation words is one, acquiring text information corresponding to the operation words; acquiring entity word information corresponding to the text information; and generating corresponding question-return statement information according to the entity word information.
In an embodiment of the application, the acquiring entity word information corresponding to the text information includes: acquiring service information corresponding to the problem to be replied; acquiring an entity word information set corresponding to the service information; and acquiring entity word information corresponding to the text information from the entity word information set.
According to the information reply method, in the process of the received reply information of the problem to be replied, the similarity between the problem to be replied and each preset problem is compared with a preset similarity threshold, and under the condition that the similarity between the problem to be replied and each preset problem is determined to be smaller than the preset similarity threshold, named entity recognition is carried out on the problem to be replied so as to obtain the named entity recognition result of the problem to be replied, and the reply information of the problem to be replied is determined and output according to the named entity recognition result. Therefore, when the similarity between the received problem to be replied and each preset problem is smaller than a preset similarity threshold, the problem to be replied is subjected to named entity recognition, and reply information of the problem to be replied is determined according to the recognition result of the named entity of the problem to be replied. Therefore, the problem to be replied is replied by combining the named entity recognition result of the problem to be replied, and the man-machine interaction experience is improved.
Another embodiment of the present application provides an information replying apparatus, including: the acquisition module is used for acquiring the problem to be replied; the named entity recognition module is used for carrying out named entity recognition on the problem to be replied under the condition that the similarity between the problem to be replied and each preset problem is smaller than a preset similarity threshold value so as to obtain a named entity recognition result of the problem to be replied; the first determining module is used for determining reply information of the problem to be replied according to the named entity recognition result; and the output module is used for outputting the reply information.
In one embodiment of the present application, the named entity identification module includes: and the named entity recognition unit is used for inputting the problem to be replied into a pre-trained named entity recognition model so as to obtain a named entity recognition result of the problem to be replied.
In an embodiment of the present application, the named entity recognition module includes a semantic representation layer, a conditional random field layer, and a full connection layer, which are connected in sequence, and the named entity recognition unit is specifically configured to: inputting the question to be replied into the semantic representation layer to obtain semantic representation characteristics of the question to be replied; inputting the semantic representation characteristics into the conditional random field layer to obtain the probability of each participle in the problem to be replied corresponding to the named entity category; and inputting the probability of the named entity category corresponding to each participle into a full connection layer to obtain a named entity recognition result corresponding to the problem to be replied.
In one embodiment of the present application, the apparatus comprises: the second determining module is used for determining the category number of the named entity categories according to the named entity identification result; and the first reply determining module is used for generating corresponding question-answering sentence information according to the text information corresponding to the named entity category under the condition that the category number is one, and taking the question-answering sentence information as the reply information of the question to be replied.
In one embodiment of the present application, the apparatus further comprises: the disassembling module is used for disassembling the problem to be replied under the condition that the number of the categories is multiple, so that the problem to be replied is disassembled into multiple sentences; the first question-back sentence determining module is used for determining the named entity type of each sentence and generating question-back sentence information corresponding to the sentence according to the text information corresponding to the named entity type; and the second reply determining module is used for generating reply information of the question to be replied according to the question-asking information corresponding to each statement.
In an embodiment of the application, the first question-return statement determining module is specifically configured to: judging whether the number of the operation words is multiple or not under the condition that the named entity type is the operation words; and if the number of the operation words is multiple, generating corresponding question-back sentence information according to the text information corresponding to each operation word.
In one embodiment of the present application, the apparatus further comprises: the second question-back sentence determining module is used for acquiring text information corresponding to the operation words if the number of the operation words is one; acquiring entity word information corresponding to the text information; and generating corresponding question-return statement information according to the entity word information.
In an embodiment of the application, the second question-return statement determining module is specifically configured to: acquiring service information corresponding to the problem to be replied; acquiring an entity word information set corresponding to the service information; and acquiring entity word information corresponding to the text information from the entity word information set.
The information replying device comprises a receiving module, a comparing module and a setting module, wherein the comparing module is used for comparing the similarity between the problem to be replied and each preset problem with a preset similarity threshold value in the process of received reply information of the problem to be replied, and under the condition that the similarity between the problem to be replied and each preset problem is determined to be smaller than the preset similarity threshold value, the recognizing module is used for recognizing the named entity of the problem to be replied to obtain the recognition result of the named entity of the problem to be replied, and according to the recognition result of the named entity, the reply information of the problem to be replied is determined and output. Therefore, when the similarity between the received problem to be replied and each preset problem is smaller than a preset similarity threshold, the problem to be replied is subjected to named entity recognition, and reply information of the problem to be replied is determined according to the recognition result of the named entity of the problem to be replied. Therefore, the problem to be replied is replied by combining the named entity recognition result of the problem to be replied, and the man-machine interaction experience is improved.
An embodiment of another aspect of the present application provides an electronic device, including: the information replying method comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the program, the information replying method in the embodiment of the application is realized.
Another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor, and the information replying method in the embodiment of the present application is provided.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
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The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a schematic flowchart of an information reply method according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating another information replying method according to an embodiment of the present disclosure.
Fig. 3 is a detailed flowchart of an information reply method according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an information replying device according to an embodiment of the present application.
Fig. 5 is a schematic structural diagram of an information replying device according to another embodiment of the present application.
FIG. 6 is a block diagram of an electronic device according to one embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes an information reply method, an information reply device, and an electronic device according to an embodiment of the present application with reference to the drawings.
Fig. 1 is a schematic flowchart of an information reply method according to an embodiment of the present application. It should be noted that an execution main body of the information reply method provided in this embodiment is an information reply device, where the information reply device may be implemented in a software and/or hardware manner, the information reply device in this embodiment may be configured in an electronic device, the electronic device in this embodiment may include a terminal device, a server, and the like, and the embodiment does not specifically limit the electronic device.
Fig. 1 is a schematic flowchart of an information reply method according to an embodiment of the present application.
As shown in fig. 1, the information replying method may include:
step 101, a question to be replied is obtained.
Specifically, the questions to be replied received in the present application refer to all questions posed by the user.
And 102, under the condition that the similarity between the problem to be replied and each preset problem is smaller than a preset similarity threshold, carrying out named entity recognition on the problem to be replied to obtain a named entity recognition result of the problem to be replied.
In this embodiment, after the problem to be replied is obtained, the problem may be matched with each problem in the knowledge base to obtain the similarity between the problem and each problem.
In this embodiment, when determining the similarity between the to-be-replied question and each preset question, it may be determined whether the similarity between the to-be-replied question and each preset question is smaller than a preset similarity threshold, and if both are smaller than the similarity threshold, at this time, the named entity recognition may be performed on the received to-be-replied question according to a preset manner, so as to obtain a named entity recognition result of the to-be-replied question.
In some embodiments, the named entity recognition may be performed on the received question to be replied through a preset named entity recognition rule, so as to obtain a named entity recognition result of the question to be replied.
Specifically, to take a simple example, the sentence "Xiaozhangying 8 am going to school class. "in, named entity identification should be able to extract information, name: small, time: morning 8, site: school.
Among them, Named Entity Recognition (NER) is an important basic tool in application fields such as information extraction, question-answering system, syntactic analysis, machine translation, and plays an important role in the process of putting natural language processing technology into practical use. Generally speaking, the task of named entity recognition is to identify named entities in three major categories (entity category, time category and number category), seven minor categories (person name, organization name, place name, time, date, currency and percentage) in the text to be processed.
And 103, determining reply information of the question to be replied according to the named entity recognition result.
And 104, outputting the reply information.
In some embodiments, the smart machine may display the reply information on the interactive interface. In some embodiments, the smart machine may output the reply message by way of voice play. In other embodiments, the intelligent machine may also output the reply message simultaneously through the interactive interface and the voice playing mode. The intelligent machine outputs the reply message is not limited thereto, and the manner of outputting the reply message is not particularly limited in this embodiment.
In the information reply method in the embodiment of the application, in the process of the received reply information of the problem to be replied, the similarity between the problem to be replied and each preset problem is compared with a preset similarity threshold, and under the condition that the similarity between the problem to be replied and each preset problem is determined to be smaller than the preset similarity threshold, named entity recognition is carried out on the problem to be replied so as to obtain a named entity recognition result of the problem to be replied, and the reply information of the problem to be replied is determined and the reply information is output according to the named entity recognition result. Therefore, when the similarity between the received problem to be replied and each preset problem is smaller than a preset similarity threshold, the problem to be replied is subjected to named entity recognition, and reply information of the problem to be replied is determined according to the recognition result of the named entity of the problem to be replied. Therefore, the problem to be replied is replied by combining the named entity recognition result of the problem to be replied, and the man-machine interaction experience is improved.
Fig. 2 is a flowchart illustrating another information replying method according to an embodiment of the present disclosure.
As shown in fig. 2, the method may include:
step 201, a question to be replied is obtained.
Step 202, under the condition that the similarity between the problem to be replied and each preset problem is smaller than a preset similarity threshold, inputting the problem to be replied into a named entity recognition model trained in advance to obtain a named entity recognition result of the problem to be replied.
In some embodiments of the present application, in order to accurately determine the named entity recognition result of the to-be-replied question through the named recognition model, the named recognition model in this embodiment may include a semantic representation layer, a conditional random field layer, and a full connection layer, which are sequentially connected, and the to-be-replied question is input to the pre-trained named entity recognition model, so as to obtain the named entity recognition result of the to-be-replied question in a possible implementation manner that: inputting the question to be replied into a semantic representation layer to obtain semantic representation characteristics of the question to be replied; inputting the semantic representation characteristics into a conditional random field layer to obtain the probability of each participle in the problem to be replied corresponding to the named entity category; and inputting the probability of the named entity category corresponding to each participle into the full connection layer to obtain the named entity recognition result corresponding to the problem to be replied.
In this embodiment, in order to accurately determine the recognition result of the named entity of the to-be-replied question through the named entity recognition model, the named entity recognition model may be trained in combination with training data. The exemplary embodiment of training the named recognition model is as follows: the method comprises the steps of obtaining training data, wherein the training data comprise sample problems and named entity identification marking data of the sample problems, inputting the sample problems into a semantic representation model to obtain semantic representation characteristics of the sample problems, inputting the semantic representation characteristics into a conditional random field layer to obtain the probability of each participle in a problem to be replied corresponding to a named entity category, inputting the probability of each participle corresponding to the named entity category into a full connection layer to obtain a named entity identification result corresponding to the problem to be replied, and adjusting parameters of the conditional random field layer and the full connection layer in the named entity identification model according to the named entity identification result and the named entity identification marking data until the named entity model meets preset conditions to obtain the trained named entity identification model. In some embodiments, the semantic representation layer may be a pre-trained language model in order to accurately represent semantic features of the questions to be replied. As an exemplary embodiment, the pre-training Language model may be a bert (bidirectional Encoder retrieval from transforms) model, or an enhanced semantic Representation model ernie (enhanced Language retrieval with information entities).
The BERT model is a pre-training language model which is learned by performing unsupervised learning pre-training on large-scale unmarked corpus by using a transformer architecture; the goal of the BERT model is to obtain the rich semantic information-containing Representation of the text using large-scale markerless corpus training [ i.e.: semantic representation of text ]. The semantic representation of the text is then fine-tuned in a specific Natural Language Processing (NLP) task, and ultimately applied to the NLP task. BERT training procedure brief introduction: BERT is also pre-trained. Its weight is learned beforehand by two unsupervised tasks. These two tasks are: a Masked Language Model (MLM) and a next sentence prediction (next sense prediction). BERT applies mode profile-BERT does not need to be trained from scratch for every new task. Instead, fine-tuning (fine-tuning) is performed on the pre-trained weights.
In some embodiments, the conditional random field layer may be a conditional random field model. Conditional random fields CRF (conditional random fields) are a kind of discriminative probability model, which is a kind of random field commonly used for labeling or analyzing sequence data, and is a conditional probability distribution model P (Y | X) representing a markov random field of another set of output random variables Y given a set of input random variables X, that is, CRF is characterized by assuming that the output random variables constitute a markov random field. Conditional random fields can be viewed as a generalization of the maximum entropy markov model over the labeling problem.
Among them, the fully connected layers (FC) play a role of "classifier" in the whole convolutional neural network. If we say that operations such as convolutional layers, pooling layers, and activation function layers map raw data to hidden layer feature space, the fully-connected layer serves to map the learned "distributed feature representation" to the sample label space. In practical use, the fully-connected layer may be implemented by a convolution operation: a fully-connected layer that is fully-connected to the previous layer may be converted to a convolution with a convolution kernel of 1x 1; while the fully-connected layer whose preceding layer is a convolutional layer can be converted to a global convolution with a convolution kernel of hxw, h and w being the height and width of the preceding layer convolution result, respectively.
Step 203, determining reply information of the question to be replied according to the named entity recognition result.
In an embodiment of the present application, in order to accurately determine the reply information of the to-be-replied question, one possible implementation manner of determining the reply information of the to-be-replied question according to the named entity identification result is as follows: determining the category number of the named entity categories according to the named entity identification result; and under the condition that the number of the categories is one, generating corresponding question-answering sentence information according to the text information corresponding to the named entity categories, and taking the question-answering sentence information as reply information of the question to be replied.
In some embodiments, under the condition that the number of the categories is multiple, the problem to be replied is disassembled to disassemble the problem to be replied into multiple sentences; determining a named entity type of each statement, and generating question-back statement information corresponding to the statement according to text information corresponding to the named entity type; and generating reply information of the question to be replied according to the question return information corresponding to each statement.
In some embodiments of the present application, in a case where the name recognition category is an entity word type, corresponding question-back sentence information may be generated according to text information corresponding to the entity word.
For example, in a case where it is determined that the named entity category of the question to be answered is the entity word category, and the entity word type corresponds to a "white bar", the generated question-back sentence information may be "ask, what question you want to consult the white bar? ".
For another example, the question to be replied is "what is the white bar of i today, what is the gold bar? "the password entity category in the question to be replied may be determined as an entity word category through named entity recognition, and the text information corresponding to the entity word category is" white bar "and" gold bar ", and the generated question-answering information may be" according to the text information corresponding to the entity word type: ask, which question you want to ask for the white bar, gold bar? ".
In other embodiments, when the named entity type is an attribute word type, corresponding question-back statement information may be generated according to text information corresponding to the attribute word type.
For example, in the case that the named entity category of the question to be answered is determined as the attribute word category, and the attribute word corresponds to the "quota", the generated question-asking sentence information may be "ask, what service is you want to know about the quota question? ".
For another example, the question to be answered is "amount and balance, the named entity identification is performed on the question to be answered, the type of the named entity of the question to be answered is determined to be an attribute word type, two attribute words are" amount "and" balance ", at this time, according to the text information corresponding to the attribute word type, the generated question-answering sentence information is" ask, and you want to know which question in the amount and balance.
In other embodiments, when the named entity category is an operation word, determining whether the number of the operation words is multiple; and if the number of the operation words is multiple, generating corresponding question-back sentence information according to the text information corresponding to each operation word.
For example, when the named entity category of the question to be answered is determined as the operation word, and the "how to open, how to activate, and how to query" corresponding to the operation word, the generated question-answering sentence information may be "ask, who wants to know how to open, how to activate, and which question to query? ".
In other embodiments, if the number of the operation words is one, the text information corresponding to the operation words is obtained, the entity word information corresponding to the text information is obtained, and the corresponding question-back sentence information is generated according to the entity word information.
For example, when the named entity category of the question to be answered is determined as the operation word, and if the operation word corresponds to "how to open", the generated question-back sentence information may be "ask, which one of the open questions in the common white bar, the gold bar, and the small vault wants to know? ".
It can be understood that, in different application scenarios, the above manner of obtaining the entity word information corresponding to the text information is different, and is illustrated as follows:
as an exemplary implementation manner, the entity word information corresponding to the text information may be obtained according to a correspondence relationship between the text information corresponding to the operation word and the entity word information that is stored in advance.
It should be noted that the text information and the entity word information corresponding to the operation word in the corresponding relationship are identified by a named entity identification model. Specifically, a plurality of entity words corresponding to each operation word may be counted, and a corresponding relationship between the operation word and the entity word may be established.
As another exemplary embodiment, in order to quickly and accurately determine the entity word information corresponding to the corresponding text information, one possible implementation manner of acquiring the entity word information corresponding to the text information is as follows: and acquiring corresponding service information, acquiring an entity word information set corresponding to the service information, and acquiring entity word information corresponding to the text information from the entity word information set.
The service information may include service name information and/or service identification information, and the embodiment is not limited in this respect.
And step 204, outputting the reply message.
In the information reply method in the embodiment of the application, in the process of the received reply information of the problem to be replied, the similarity between the problem to be replied and each preset problem is compared with a preset similarity threshold, and under the condition that the similarity between the problem to be replied and each preset problem is determined to be smaller than the preset similarity threshold, named entity recognition is carried out on the problem to be replied so as to obtain a named entity recognition result of the problem to be replied, and the reply information of the problem to be replied is determined and the reply information is output according to the named entity recognition result. Therefore, when the similarity between the received problem to be replied and each preset problem is smaller than a preset similarity threshold, the problem to be replied is subjected to named entity recognition, and reply information of the problem to be replied is determined according to the recognition result of the named entity of the problem to be replied. Therefore, the problem to be replied is replied by combining the named entity recognition result of the problem to be replied, and the man-machine interaction experience is improved.
In order to make the technical solutions of the present application clearly understood by those skilled in the art, the technical solutions of this embodiment are further described below with reference to fig. 3, it should be noted that subj in fig. 3 represents an entity word, pred represents an operation word, and prop represents an attribute word.
Specifically, in step 301, after the user inputs a query term (query), the query term may be used as a question to be replied. Correspondingly, the named entity recognition model can be carried out on the query word through the named entity recognition model so as to obtain the named entity recognition result of the question to be replied.
Correspondingly, step 302, several categories in the named entity recognition result are determined. In this embodiment, three categories are taken as an example, and are an entity word category, an operation word category and an attribute word category. That is, several categories in the named entity recognition result are judged.
Step 303, judging the category in the named entity recognition result, judging whether the named entity recognition result is a multi-sentence word, and if not, outputting a clarification word; if the sentence is multiple, the sentence is split, and the sentence is split into a plurality of single element lists.
Step 304 may perform a single element range based on one of three categories in the case where one category is included in the named entity recognition result:
in step 305, if the category number identified by the named entity only contains one sub j, ask "ask, what question you want to ask sub j? "; if > 2 or more subj are included, ask "ask a question, which question of subj1, subj2, subj3.. will you consult? "
Step 306, if only one prop is included, ask a question in return, "ask a question, what service should you like to know about prop? "; if > 2 or more prop, ask a question in return, which question you want prop1, prop2, prop3 …? "
Step 307, if only one pred is contained, returning an entity question return list according to the pred; if > 2 or more preds are included, ask a question back, which question you want to consult among pred1, pred2, and pred3 …? "
In some embodiments, where multiple types are included in the named entity recognition result, multiple element processing logic may be employed, with punctuation and single element processing logic processing per sentence.
An exemplary procedure for asking back the post-logic process is as follows: three slots are opened in redis before the question-back logic is performed: subj, pred, prop, are initially empty, and if there is only one subj, ask "ask, what question you want to ask subj? ". After a user inputs a query, a model identifies a subj, the subj is filled into a subj slot position, after the user answers a question, element NER identification is carried out according to the question answered by the user, pred or prop is identified, the corresponding slot position is filled, question splicing is carried out, response logic is triggered again, and all the slot positions are cleared until field words are switched. If > 2 or more subj are included, ask "ask a question, which question of subj1, subj2, subj3.. will you consult? ". The user answers subj, fills the slot corresponding to subj, goes through the order element question-back logic, and queries to continue filling the slot until finding faq answer.
Among them, redis called: remote Dictionary Server (Remote data service), is a memory-based high-performance key-value database. faq is an abbreviation for freqently assigned Questions, the "Frequently Asked question".
In the information reply method in the embodiment of the application, in the process of the received reply information of the problem to be replied, the similarity between the problem to be replied and each preset problem is compared with a preset similarity threshold, and under the condition that the similarity between the problem to be replied and each preset problem is determined to be smaller than the preset similarity threshold, the problem to be replied is named and recognized through a pre-trained named entity recognition model so as to obtain the named entity recognition result of the problem to be replied, and the reply information of the problem to be replied is determined and the reply information is output according to the named entity recognition result. Therefore, when the similarity between the received problem to be replied and each preset problem is smaller than a preset similarity threshold, the problem to be replied is subjected to named entity recognition, and reply information of the problem to be replied is determined according to the recognition result of the named entity of the problem to be replied. Therefore, the problem to be replied is replied by combining the named entity recognition result of the problem to be replied, and the man-machine interaction experience is improved.
FIG. 4 is a schematic structural diagram of an information replying device according to an embodiment of the present application
As shown in fig. 4, the information replying apparatus 400 includes:
an obtaining module 401, configured to obtain a question to be replied.
And a named entity identification module 402, configured to perform named entity identification on the problem to be replied to obtain a named entity identification result of the problem to be replied, when the similarity between the problem to be replied and each preset problem is smaller than a preset similarity threshold.
The first determining module 403 is configured to determine reply information of the question to be replied according to the named entity identification result.
And an output module 404, configured to output the reply message.
In the information replying device in the embodiment of the application, in the process of the received reply information of the problem to be replied, the similarity between the problem to be replied and each preset problem is compared with the preset similarity threshold, and under the condition that the similarity between the problem to be replied and each preset problem is determined to be smaller than the preset similarity threshold, named entity recognition is carried out on the problem to be replied so as to obtain the named entity recognition result of the problem to be replied, and the reply information of the problem to be replied is determined and the reply information is output according to the named entity recognition result. Therefore, when the similarity between the received problem to be replied and each preset problem is smaller than a preset similarity threshold, the problem to be replied is subjected to named entity recognition, and reply information of the problem to be replied is determined according to the recognition result of the named entity of the problem to be replied. Therefore, the problem to be replied is replied by combining the named entity recognition result of the problem to be replied, and the man-machine interaction experience is improved.
In an embodiment of the present application, as shown in fig. 5, the named entity identifying module 402 may include:
the named entity recognition unit 4021 is configured to input the to-be-replied question to a pre-trained named entity recognition model to obtain a named entity recognition result of the to-be-replied question.
In an embodiment of the present application, the named entity identifying unit 4021 is specifically configured to:
inputting the question to be replied into a semantic representation layer to obtain semantic representation characteristics of the question to be replied;
inputting the semantic representation characteristics into a conditional random field layer to obtain the probability of each participle in the problem to be replied corresponding to the named entity category;
and inputting the probability of the named entity category corresponding to each participle into the full connection layer to obtain the named entity recognition result corresponding to the problem to be replied.
In one embodiment of the present application, as shown in fig. 5, the apparatus may further include:
a second determining module 405, configured to determine the category number of the named entity category according to the named entity identification result.
The first reply determining module 406 is configured to, when the number of categories is one, generate corresponding question-answering sentence information according to the text information corresponding to the named entity category, and use the question-answering sentence information as reply information of the question to be replied.
A disassembling module 407, configured to disassemble the problem to be replied into multiple statements when the number of categories is multiple.
A first question-back sentence determining module 408, configured to determine, for each sentence, a named entity category of the sentence, and generate question-back sentence information corresponding to the sentence according to text information corresponding to the named entity category;
the second reply determining module 409 is configured to generate reply information of the question to be replied according to the question-return information corresponding to each statement.
In an embodiment of the application, the first question-back statement determining module 408 is specifically configured to: and under the condition that the named entity type is the operation word, judging whether the number of the operation words is multiple or not, and if the number of the operation words is multiple, generating corresponding question-back sentence information according to the text information corresponding to each operation word.
In one embodiment of the present application, as shown in fig. 5, the apparatus may further include:
a second question-return determining module 410, configured to, if the number of the operation words is one, obtain text information corresponding to the operation words; acquiring entity word information corresponding to the text information; and generating corresponding question-return statement information according to the entity word information.
In an embodiment of the present application, the second question-back statement determining module 410 is specifically configured to: acquiring service information corresponding to the problem to be replied; acquiring an entity word information set corresponding to the service information; and acquiring entity word information corresponding to the text information from the entity word information set.
In the information replying device in the embodiment of the application, in the process of the received reply information of the problem to be replied, the similarity between the problem to be replied and each preset problem is compared with the preset similarity threshold, and under the condition that the similarity between the problem to be replied and each preset problem is determined to be smaller than the preset similarity threshold, named entity recognition is carried out on the problem to be replied so as to obtain the named entity recognition result of the problem to be replied, and the reply information of the problem to be replied is determined and the reply information is output according to the named entity recognition result. Therefore, when the similarity between the received problem to be replied and each preset problem is smaller than a preset similarity threshold, the problem to be replied is subjected to named entity recognition, and reply information of the problem to be replied is determined according to the recognition result of the named entity of the problem to be replied. Therefore, the problem to be replied is replied by combining the named entity recognition result of the problem to be replied, and the man-machine interaction experience is improved.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
FIG. 6 is a block diagram of an electronic device according to an embodiment of the present application.
As shown in fig. 6, the electronic device includes:
memory 601, processor 602, and computer instructions stored on memory 601 and executable on processor 602.
The processor 602, when executing the instructions, implements the message reply method provided in the above embodiments.
Further, the electronic device further includes:
a communication interface 603 for communication between the memory 601 and the processor 602.
Memory 601 for storing computer instructions executable on processor 602.
Memory 601 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor 602 is configured to implement the information replying method of the foregoing embodiments when executing the program.
If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a chip, the memory 601, the processor 602, and the communication interface 603 may complete mutual communication through an internal interface.
The processor 602 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (18)

1. An information replying method, characterized in that the method comprises:
acquiring a question to be replied;
under the condition that the similarity between the problem to be replied and each preset problem is smaller than a preset similarity threshold, conducting named entity recognition on the problem to be replied to obtain a named entity recognition result of the problem to be replied;
determining reply information of the question to be replied according to the named entity recognition result;
and outputting the reply information.
2. The method of claim 1, wherein the performing named entity recognition on the question to be replied to obtain a named entity recognition result of the question to be replied comprises:
and inputting the problem to be replied to a pre-trained named entity recognition model to obtain a named entity recognition result of the problem to be replied.
3. The method as claimed in claim 2, wherein the named recognition model comprises a semantic representation layer, a conditional random field layer and a full connection layer which are connected in sequence, and the inputting the question to be replied to a pre-trained named entity recognition model to obtain the named entity recognition result of the question to be replied comprises:
inputting the question to be replied into the semantic representation layer to obtain semantic representation characteristics of the question to be replied;
inputting the semantic representation characteristics into the conditional random field layer to obtain the probability of each participle in the problem to be replied corresponding to the named entity category;
and inputting the probability of the named entity category corresponding to each participle into a full connection layer to obtain a named entity recognition result corresponding to the problem to be replied.
4. The method as claimed in claim 1, wherein the determining the reply information of the question to be replied according to the named entity recognition result comprises:
determining the category number of the named entity categories according to the named entity identification result;
and under the condition that the number of the categories is one, generating corresponding question-answering sentence information according to the text information corresponding to the named entity categories, and taking the question-answering sentence information as the reply information of the question to be replied.
5. The method of claim 4, wherein the method further comprises:
under the condition that the number of the categories is multiple, disassembling the problem to be replied to disassemble the problem to be replied into a plurality of sentences;
determining a named entity type of each statement, and generating question-back statement information corresponding to the statement according to text information corresponding to the named entity type;
and generating reply information of the question to be replied according to the question-asking statement information corresponding to each statement.
6. The method of claim 4, wherein generating corresponding question-back information according to the text information corresponding to the named entity category comprises:
judging whether the number of the operation words is multiple or not under the condition that the named entity type is the operation words;
and if the number of the operation words is multiple, generating corresponding question-back sentence information according to the text information corresponding to each operation word.
7. The method of claim 6, wherein the method further comprises:
if the number of the operation words is one, acquiring text information corresponding to the operation words;
acquiring entity word information corresponding to the text information;
and generating corresponding question-return statement information according to the entity word information.
8. The method of claim 7, wherein the obtaining entity word information corresponding to the text information comprises:
acquiring service information corresponding to the problem to be replied;
acquiring an entity word information set corresponding to the service information;
and acquiring entity word information corresponding to the text information from the entity word information set.
9. An information replying device, comprising:
the acquisition module is used for acquiring the problem to be replied;
the named entity recognition module is used for carrying out named entity recognition on the problem to be replied under the condition that the similarity between the problem to be replied and each preset problem is smaller than a preset similarity threshold value so as to obtain a named entity recognition result of the problem to be replied;
the first determining module is used for determining reply information of the problem to be replied according to the named entity recognition result;
and the output module is used for outputting the reply information.
10. The apparatus of claim 9, wherein the named entity identification module comprises:
and the named entity recognition unit is used for inputting the problem to be replied into a pre-trained named entity recognition model so as to obtain a named entity recognition result of the problem to be replied.
11. The apparatus according to claim 10, wherein the named entity recognition model comprises a semantic representation layer, a conditional random field layer, and a full connection layer connected in sequence, and the named entity recognition unit is specifically configured to:
inputting the question to be replied into the semantic representation layer to obtain semantic representation characteristics of the question to be replied;
inputting the semantic representation characteristics into the conditional random field layer to obtain the probability of each participle in the problem to be replied corresponding to the named entity category;
and inputting the probability of the named entity category corresponding to each participle into a full connection layer to obtain a named entity recognition result corresponding to the problem to be replied.
12. The apparatus of claim 9, wherein the apparatus comprises:
the second determining module is used for determining the category number of the named entity categories according to the named entity identification result;
and the first reply determining module is used for generating corresponding question-answering sentence information according to the text information corresponding to the named entity category under the condition that the category number is one, and taking the question-answering sentence information as the reply information of the question to be replied.
13. The apparatus of claim 12, wherein the apparatus further comprises:
the disassembling module is used for disassembling the problem to be replied under the condition that the number of the categories is multiple, so that the problem to be replied is disassembled into multiple sentences;
the first question-back sentence determining module is used for determining the named entity type of each sentence and generating question-back sentence information corresponding to the sentence according to the text information corresponding to the named entity type;
and the second reply determining module is used for generating reply information of the question to be replied according to the question-asking information corresponding to each statement.
14. The apparatus of claim 12, wherein the first question-back statement determination module is specifically configured to:
judging whether the number of the operation words is multiple or not under the condition that the named entity type is the operation words;
and if the number of the operation words is multiple, generating corresponding question-back sentence information according to the text information corresponding to each operation word.
15. The apparatus of claim 14, wherein the apparatus further comprises:
the second question-back sentence determining module is used for acquiring text information corresponding to the operation words if the number of the operation words is one; acquiring entity word information corresponding to the text information; and generating corresponding question-return statement information according to the entity word information.
16. The apparatus of claim 15, wherein the second question-back statement determination module is specifically configured to:
acquiring service information corresponding to the problem to be replied;
acquiring an entity word information set corresponding to the service information;
and acquiring entity word information corresponding to the text information from the entity word information set.
17. An electronic device, comprising:
memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the information reply method according to any of claims 1 to 8 when executing the program.
18. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the information reply method according to any one of claims 1 to 8.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115525804A (en) * 2022-09-23 2022-12-27 中电金信软件有限公司 Information query method and device, electronic equipment and storage medium

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180158459A1 (en) * 2016-12-06 2018-06-07 Panasonic Intellectual Property Management Co., Ltd. Information processing method, information processing apparatus, and non-transitory recording medium
US20200387530A1 (en) * 2019-06-10 2020-12-10 International Business Machines Corporation Identifying named entities in questions related to structured data
CN112287090A (en) * 2020-11-23 2021-01-29 深圳季连科技有限公司 Financial question asking back method and system based on knowledge graph
US20210081614A1 (en) * 2020-11-09 2021-03-18 The Abstract Operations Company Systems and methods for predicting mappings between named entities and parameters via machine learning techniques

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180158459A1 (en) * 2016-12-06 2018-06-07 Panasonic Intellectual Property Management Co., Ltd. Information processing method, information processing apparatus, and non-transitory recording medium
US20200387530A1 (en) * 2019-06-10 2020-12-10 International Business Machines Corporation Identifying named entities in questions related to structured data
US20210081614A1 (en) * 2020-11-09 2021-03-18 The Abstract Operations Company Systems and methods for predicting mappings between named entities and parameters via machine learning techniques
CN112287090A (en) * 2020-11-23 2021-01-29 深圳季连科技有限公司 Financial question asking back method and system based on knowledge graph

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115525804A (en) * 2022-09-23 2022-12-27 中电金信软件有限公司 Information query method and device, electronic equipment and storage medium

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