CN111177343A - Method and system for automatically constructing medical and American inquiry guide logic - Google Patents
Method and system for automatically constructing medical and American inquiry guide logic Download PDFInfo
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- CN111177343A CN111177343A CN201911300806.1A CN201911300806A CN111177343A CN 111177343 A CN111177343 A CN 111177343A CN 201911300806 A CN201911300806 A CN 201911300806A CN 111177343 A CN111177343 A CN 111177343A
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Abstract
The invention discloses a method for automatically constructing medical and American inquiry guide logic, which comprises the following steps: s1: acquiring dialogue data of at least two hospitals, and extracting customer service reply dialogs in the dialogue data, wherein the customer service reply dialogs comprise an inquiry dialogs and a non-inquiry dialogs; s2: extracting the inquiry dialogues for analysis, and dividing the inquiry dialogues into a plurality of inquiry categories; s3: and analyzing the frequency of the dialogue data in different inquiry categories to obtain the optimal inquiry logic and the optimal inquiry dialogue of the subject item.
Description
Technical Field
The invention relates to the field of medical plastic, in particular to a method and a system for automatically constructing medical and American inquiry guide logic.
Background
The existing conversation robots are mainly used for leading conversation by visitors, and the robots are passive. But in the medical plastic industry, the robot is required to actively talk and guide visitors. The conventional solution is to trigger an inquiry based on a visitor sending a keyword or to dominate the conversation by writing a general inquiry guide flow. This form of dialog content can be relatively rigid and unable to understand the client semantics.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a robot inquiry mechanism based on a theme scene, which understands the semantics of visitors, leads a conversation process and helps a robot to obtain more visitor information to perform more accurate symptom analysis, thereby providing a system and a method for automatically constructing medical and beauty inquiry guide logic. The method comprises the following specific steps:
a method for automatically constructing medical and American inquiry guide logic comprises the following steps:
s1: acquiring dialogue data of at least two hospitals, and extracting customer service reply dialogs in the dialogue data, wherein the customer service reply dialogs comprise an inquiry dialogs and a non-inquiry dialogs;
s2: extracting the inquiry dialogues for analysis, and dividing the inquiry dialogues into a plurality of inquiry categories;
s3: and analyzing the frequency of the dialogue data in different inquiry categories to obtain the optimal inquiry logic and the optimal inquiry dialogue of the subject item.
Preferably, the step S2 further includes:
s2-1: crawling the shaping project information of the official website of each hospital in the step S1 through a crawler, and counting common projects;
s2-2: classifying the dialogue data according to the common items to form item data;
s2-3: extracting an inquiry call in the customer service answer call in the step S1 according to each type of project data;
preferably, the step S3 further includes:
s3-1: extracting high-quality conversations in the conversation data, analyzing the frequency of the conversations in different inquiry categories, and selecting the permutation and combination with the highest occurrence frequency as the inquiry logic of the subject item;
s3-2: and selecting the inquiry dialogs with the highest use frequency according to each inquiry category to obtain the optimal inquiry logic and the optimal inquiry dialogs of the inquiry categories.
Preferably, step S2 employs a BERT language model and a binary classification model.
Preferably, the interviewing is performed based on statistical and/or machine learning results.
Preferably, the premium conversations include conversations that exceed three rounds without negative emotions by the user.
According to another aspect of the present invention, there is provided a system for automatically constructing a medical and cosmetic inquiry guide logic, comprising:
the system comprises a data module, a category module and an analysis module; wherein the content of the first and second substances,
the data module is used for acquiring conversation data of at least two hospitals and extracting customer service reply dialogs in the conversation data, wherein the customer service reply dialogs comprise an inquiry dialogs and a non-inquiry dialogs;
the category module is used for extracting the inquiry dialogs for analysis and dividing the inquiry dialogs into a plurality of inquiry categories;
and the analysis module is used for analyzing the frequency of the dialogue data in different inquiry categories to obtain the optimal inquiry logic and the optimal inquiry dialogue of the subject project.
Preferably, the classification module is further configured to:
(1) crawling the shaping project information of the official website of each hospital through a crawler, and counting common projects;
(2) classifying the dialogue data according to the common items to form item data;
(3) and extracting the inquiry dialogs in the customer service reply dialogs according to the data of each type of items.
Preferably, the analysis module is further configured to:
(1) extracting high-quality conversations in the conversation data, analyzing the frequency of the conversations in different inquiry categories, and selecting the permutation and combination with the highest occurrence frequency as the inquiry logic of the subject item;
(2) and selecting the inquiry dialogs with the highest use frequency according to each inquiry category to obtain the optimal inquiry logic and the optimal inquiry dialogs of the inquiry categories.
Preferably, the item module includes a BERT language model and a binary model.
Preferably, the interviewing is performed based on statistical and/or machine learning results.
Preferably, the premium conversations include conversations that exceed three rounds without negative emotions by the user.
Compared with the prior art, the invention has the following advantages:
1. active inquiry guidance can be performed according to the project;
2. the inquiry logic and the inquiry dialect are automatically generated according to the results of statistics and machine learning, so that manpower and material resources are saved, and the optimal inquiry logic and the optimal inquiry dialect can be obtained according to the big data.
Drawings
FIG. 1 is a flow chart of an interrogation guidance logic method of the present invention;
fig. 2 is a schematic structural diagram of an embodiment of the system of the present invention.
Detailed Description
Fig. 1 is a flowchart of a method for automatically constructing a doctor-beauty inquiry guide logic according to the present invention, which is a robot inquiry mechanism based on a theme scene, and is used for understanding guest semantics, leading a conversation process, and helping a robot obtain more guest information to perform more accurate symptom analysis. A method for automatically constructing medical and American inquiry guide logic comprises the following steps:
s1: acquiring dialogue data of at least two hospitals, and extracting customer service reply dialogs in the dialogue data, wherein the customer service reply dialogs comprise an inquiry dialogs and a non-inquiry dialogs;
s2: extracting the inquiry dialogues for analysis, and dividing the inquiry dialogues into a plurality of inquiry categories; the step is realized by adopting a BERT language model and a binary classification model; the method comprises the following steps:
s2-1: crawling the shaping project information of the official website of each hospital in the step S1 through a crawler, and counting common projects;
s2-2: classifying the dialogue data according to the common items to form item data;
s2-3: extracting an inquiry call in the customer service answer call in the step S1 according to each type of project data;
s3: analyzing the frequency of the dialogue data in different inquiry categories to obtain the optimal inquiry logic and the optimal inquiry dialogue of the subject project; the method comprises the following steps:
s3-1: extracting high-quality conversations in the conversation data, analyzing the frequency of the conversations in different inquiry categories, and selecting the permutation and combination with the highest occurrence frequency as the inquiry logic of the subject item;
s3-2: and selecting the inquiry dialogs with the highest use frequency according to each inquiry category to obtain the optimal inquiry logic and the optimal inquiry dialogs of the inquiry categories.
Preferably, the interviewing is performed based on statistical and/or machine learning results.
Preferably, the premium conversations include conversations that exceed three rounds without negative emotions by the user.
The method for automatically constructing the medical and American inquiry guide logic specifically comprises the following steps:
1) extracting 1-2 hospital dialogue data from different cities;
2) extracting customer service answering words;
3) crawling 1) shaping project information of a traditional Chinese medicine official website by a crawler, and counting common projects;
4) classifying the conversations according to the items; the method comprises the following steps that (1) conversation data of a plurality of items in one conversation are removed, and because the conversation quantity is large, the removal of some conversation data has no influence;
5) aiming at each type of item data, extracting an inquiry guide word from customer service answer words, which is a classification problem, dividing the customer answer words into two categories, namely an inquiry word and a non-inquiry word, wherein the adopted technical route is bert and a binary classification model;
6) analyzing the inquiry data, and dividing the inquiry dialect into a plurality of inquiry categories;
7) extracting high-quality conversations, wherein the high-quality conversations comprise more than three conversations and the conversations of which the users have no negative emotion;
8) statistical analysis step 7) frequency of different inquiry category sequences, and selecting a permutation and combination with highest occurrence frequency of the inquiry category sequences in a real conversation as an inquiry logic of the project;
9) for each interrogation category, selecting the interrogation dialogs with the highest frequency of use;
10) the best ranking of the inquiry categories (inquiry logic) and the best inquiry jargon are obtained.
The invention also provides a system for automatically constructing the doctor and beauty inquiry guide logic, so that the robot inquiry flow based on the subject project is realized, the semantics of the visitors are understood, more visitor information is obtained, and the more accurate symptom analysis and answering are further performed on the visitors.
As shown in fig. 2, fig. 2 is a schematic structural diagram of an embodiment of the system of the present invention. In this embodiment, the system includes:
the system comprises a data module, a category module and an analysis module; wherein the content of the first and second substances,
the data module is used for acquiring conversation data of at least two hospitals and extracting customer service reply dialogs in the conversation data, wherein the customer service reply dialogs comprise an inquiry dialogs and a non-inquiry dialogs;
the category module is used for extracting the inquiry dialogs for analysis and dividing the inquiry dialogs into a plurality of inquiry categories;
and the analysis module is used for analyzing the frequency of the dialogue data in different inquiry categories to obtain the optimal inquiry logic and the optimal inquiry dialogue of the subject project.
Preferably, the classification module is further configured to:
(1) crawling the shaping project information of the official website of each hospital through a crawler, and counting common projects;
(2) classifying the dialogue data according to the common items to form item data;
(3) and extracting the inquiry dialogs in the customer service reply dialogs according to the data of each type of items.
Preferably, the analysis module is further configured to:
(1) extracting high-quality conversations in the conversation data, analyzing the frequency of the conversations in different inquiry categories, and selecting the permutation and combination with the highest occurrence frequency as the inquiry logic of the subject item;
(2) and selecting the inquiry dialogs with the highest use frequency according to each inquiry category to obtain the optimal inquiry logic and the optimal inquiry dialogs of the inquiry categories.
Preferably, the item module includes a BERT language model and a binary model.
Preferably, the interviewing is performed based on statistical and/or machine learning results.
Preferably, the premium conversations include conversations that exceed three rounds without negative emotions by the user.
The embodiments in the above embodiments can be further combined or replaced, and the embodiments are only used for describing the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention, and various changes and modifications made to the technical solution of the present invention by those skilled in the art without departing from the design idea of the present invention belong to the protection scope of the present invention.
Claims (10)
1. A method for automatically constructing medical and American inquiry guide logic is characterized by comprising the following steps:
s1: acquiring dialogue data of at least two hospitals, and extracting customer service reply dialogs in the dialogue data, wherein the customer service reply dialogs comprise an inquiry dialogs and a non-inquiry dialogs;
s2: extracting the inquiry dialogues for analysis, and dividing the inquiry dialogues into a plurality of inquiry categories;
s3: and analyzing the frequency of the dialogue data in different inquiry categories to obtain the optimal inquiry logic and the optimal inquiry dialogue of the subject item.
2. The method for automatically constructing the medical and cosmetic inquiry guide logic according to claim 1, wherein the step S2 further comprises:
s2-1: crawling the shaping project information of the official website of each hospital in the step S1 through a crawler, and counting common projects;
s2-2: classifying the dialogue data according to the common items to form item data;
s2-3: the inquiry dialogs in the customer service answer dialogs of step S1 are extracted according to each type of project data.
3. The method for automatically constructing the medical and cosmetic inquiry guide logic according to claim 1, wherein the step S3 further comprises:
s3-1: extracting high-quality conversations in the conversation data, analyzing the frequency of the conversations in different inquiry categories, and selecting the permutation and combination with the highest occurrence frequency as the inquiry logic of the subject item;
s3-2: and selecting the inquiry dialogs with the highest use frequency according to each inquiry category to obtain the optimal inquiry logic and the optimal inquiry dialogs of the inquiry categories.
4. The method for automatically constructing the medical and aesthetic inquiry guide logic according to claim 1, wherein the step S2 adopts a BERT language model and a binary classification model.
5. The method for automatically constructing physician-American consultation guidance logic according to any one of claims 1 to 4, wherein the consultation dialogues are obtained according to the results of statistics and/or machine learning;
preferably, the premium conversations include conversations that exceed three rounds without negative emotions by the user.
6. A system for automatically constructing a medical and cosmetic inquiry guide logic, comprising: the system comprises a data module, a category module and an analysis module; wherein the content of the first and second substances,
the data module is used for acquiring conversation data of at least two hospitals and extracting customer service reply dialogs in the conversation data, wherein the customer service reply dialogs comprise an inquiry dialogs and a non-inquiry dialogs;
the category module is used for extracting the inquiry dialogs for analysis and dividing the inquiry dialogs into a plurality of inquiry categories;
and the analysis module is used for analyzing the frequency of the dialogue data in different inquiry categories to obtain the optimal inquiry logic and the optimal inquiry dialogue of the subject project.
7. The system for automatically constructing medical and cosmetic interrogation guide logic according to claim 6, wherein the category module is further used for:
(1) crawling the shaping project information of the official website of each hospital through a crawler, and counting common projects;
(2) classifying the dialogue data according to the common items to form item data;
(3) and extracting the inquiry dialogs in the customer service reply dialogs according to the data of each type of items.
8. The system for automatically constructing medical and cosmetic interrogation guide logic according to claim 6, wherein the analysis module is further used for:
(1) extracting high-quality conversations in the conversation data, analyzing the frequency of the conversations in different inquiry categories, and selecting the permutation and combination with the highest occurrence frequency as the inquiry logic of the subject item;
(2) and selecting the inquiry dialogs with the highest use frequency according to each inquiry category to obtain the optimal inquiry logic and the optimal inquiry dialogs of the inquiry categories.
9. The system for automatically constructing medical and cosmetic inquiry guide logic according to claim 6, wherein said item modules comprise a BERT language model and a binary classification model.
10. The system for automatically constructing physician-American consultation guidance logic according to any one of claims 7 to 10, wherein the consultation dialogues are obtained according to the results of statistics and/or machine learning;
preferably, the premium conversations include conversations that exceed three rounds without negative emotions by the user.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112185544A (en) * | 2020-09-10 | 2021-01-05 | 浙江传媒学院 | Semantic-based family medical consultation decision support method |
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| CN106570708A (en) * | 2016-10-31 | 2017-04-19 | 厦门快商通科技股份有限公司 | Management method and management system of intelligent customer service knowledge base |
| US20180218127A1 (en) * | 2017-01-31 | 2018-08-02 | Pager, Inc. | Generating a Knowledge Graph for Determining Patient Symptoms and Medical Recommendations Based on Medical Information |
| CN110543552A (en) * | 2019-09-06 | 2019-12-06 | 网易（杭州）网络有限公司 | Conversation interaction method and device and electronic equipment |
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- 2019-12-17 CN CN201911300806.1A patent/CN111177343A/en active Pending
Patent Citations (4)
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| US20180218127A1 (en) * | 2017-01-31 | 2018-08-02 | Pager, Inc. | Generating a Knowledge Graph for Determining Patient Symptoms and Medical Recommendations Based on Medical Information |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112185544A (en) * | 2020-09-10 | 2021-01-05 | 浙江传媒学院 | Semantic-based family medical consultation decision support method |
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