Intelligent question answering method and system based on natural language processing

Abstract

The invention discloses an intelligent question and answer method and system based on natural language processing. The intelligent question and answer method comprises the following steps: collecting text data from a data source; preprocessing the collected text data; analyzing the preprocessed text data, and extracting key information; constructing a knowledge graph based on the extracted key information; and answering user questions by using a knowledge graph and a natural language understanding technology. The accuracy, flexibility and efficiency of the system are improved, and information omission is reduced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent question answering, and in particular to an intelligent question answering method and system based on natural language processing. Background Art

[0002] A large amount of text data needs to be processed during information processing. However, traditional manual processing methods are inefficient and easy to miss important information. With the development of natural language processing (NLP) technology, its application in the field can greatly improve the efficiency of information processing and analysis.

[0003] Existing intelligent question-answering systems mainly rely on keyword matching and simple rule engines, which cannot deeply understand complex text content. These systems have low accuracy and reliability when processing diverse and complex text data. In addition, existing technologies are difficult to dynamically update and expand, and cannot adapt to new regulations and policies in a timely manner.

[0004] Technical problems existing in existing technologies:

[0005] Low accuracy: Existing intelligent question-answering systems mainly rely on keyword matching and are unable to accurately understand and analyze complex text content, resulting in low answer accuracy.

[0006] Poor flexibility: Existing technologies are difficult to adapt to changing regulations and policies in the field, and system updates and expansions are difficult.

[0007] Low efficiency: Traditional manual processing methods are time-consuming and labor-intensive and cannot cope with large amounts of text data.

[0008] Information omission: In large-scale data processing, it is easy to miss important key information, which affects the effectiveness of the work. Summary of the invention

[0009] In view of the above problems, the present invention is proposed to provide an intelligent question answering method and system based on natural language processing that overcomes the above problems or at least partially solves the above problems.

[0010] According to one aspect of the present invention, an intelligent question-answering method based on natural language processing is provided, the intelligent question-answering method comprising:

[0011] Collect text data from data sources;

[0012] Preprocess the collected text data;

[0013] Analyze the preprocessed text data and extract key information;

[0014] Build a knowledge graph based on the extracted key information;

[0015] Use knowledge graphs and natural language understanding technologies to answer user questions.

[0016] Optionally, the intelligent question-answering method further includes: providing a user-friendly interface and supporting multiple interaction modes.

[0017] Optionally, the text data collected from the data source specifically includes: review reports and access records.

[0018] Optionally, the preprocessing of the collected text data specifically includes: performing cleaning, word segmentation, and tagging preprocessing operations on the collected text data.

[0019] Optionally, the analysis of the preprocessed text data specifically includes: named entity recognition, syntactic analysis, and sentiment analysis.

[0020] Optionally, the constructing of a knowledge graph based on the extracted key information specifically includes: constructing a knowledge graph of a domain based on the extracted key information, displaying a variety of entities and relationships.

[0021] Optionally, answering user questions using knowledge graph and natural language understanding technology specifically includes:

[0022] Based on knowledge graph and natural language understanding technology, an intelligent question-answering system is built that can accurately understand user questions and provide corresponding answers.

[0023] Optionally, the supporting multiple interaction modes specifically includes: text and language.

[0024] The present invention also provides an intelligent question-answering system based on natural language processing, applying any of the above-mentioned intelligent question-answering methods based on natural language processing, the intelligent question-answering system comprises:

[0025] A data collection module, used to collect text data from data sources;

[0026] A data preprocessing module is used to preprocess the collected text data;

[0027] Natural language processing module, used to analyze the preprocessed text data and extract key information;

[0028] Knowledge graph construction module, used to construct a knowledge graph based on the extracted key information;

[0029] Intelligent question-answering module, used to answer user questions using knowledge graph and natural language understanding technology.

[0030] Optionally, the intelligent question-answering system further includes: a user interaction module, which is used to provide a user-friendly interface and support multiple interaction modes.

[0031] The present invention provides an intelligent question-answering method and system based on natural language processing, the intelligent question-answering method comprising: collecting text data from a data source; preprocessing the collected text data; analyzing the preprocessed text data to extract key information; constructing a knowledge graph based on the extracted key information; and answering user questions using the knowledge graph and natural language understanding technology. The accuracy, flexibility and efficiency of the system are improved, and information omission is reduced.

[0032] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 A flowchart of an intelligent question-answering method based on natural language processing provided by an embodiment of the present invention;

[0035] Figure 2 A block diagram of the composition of an intelligent question-answering system based on natural language processing provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0037] The terms "comprises" and "having" and any variations thereof in the description embodiments, claims and drawings of the present invention are intended to cover non-exclusive inclusions, for example, including a series of steps or units.

[0038] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0039] like Figure 1 As shown, an intelligent question answering method based on natural language processing includes:

[0040] Collect text data from data sources;

[0041] Clean and preprocess the collected text data.

[0042] Analyze the preprocessed text data and extract key information;

[0043] Build a knowledge graph based on the extracted key information;

[0044] Use knowledge graphs and natural language understanding technology to answer user questions;

[0045] Provides a user-friendly interface and supports multiple interaction methods.

[0046] like Figure 2 As shown, the intelligent question and answer system of the present invention includes a data collection module, a data preprocessing module, a natural language processing module, a knowledge graph construction module, an intelligent question and answer module and a user interaction module.

[0047] Data collection module: collects text data from various data sources (such as review reports, etc.).

[0048] Data preprocessing module: performs preprocessing operations such as cleaning, word segmentation, and annotation on the collected text data to provide a high-quality data foundation for subsequent natural language processing.

[0049] Natural language processing module: Use NLP technology to analyze the preprocessed text data, including named entity recognition, syntactic analysis, sentiment analysis, etc., to extract key information.

[0050] Knowledge graph construction module: Build a knowledge graph in the field based on the extracted key information to display different entities and their relationships.

[0051] Intelligent question-and-answer module: Based on knowledge graph and natural language understanding technology, an intelligent question-and-answer system is built that can accurately understand user questions and provide corresponding answers.

[0052] User interaction module: provides a friendly user interface and supports multiple interaction methods (such as text and voice) to facilitate users to query and obtain information.

[0053] Beneficial effects:

[0054] Improve the accuracy, flexibility and efficiency of the system and reduce information omissions;

[0055] Data collection and preprocessing technology: Efficiently collect and preprocess text data to ensure data quality.

[0056] Natural Language Processing Technology: Use advanced NLP technology to conduct in-depth analysis of text data and extract key information.

[0057] Knowledge graph construction technology: Build a domain-based knowledge graph to display entities and their relationships, and improve the knowledge coverage and accuracy of the question-answering system.

[0058] Intelligent question-answering technology: Build an efficient and accurate intelligent question-answering system based on knowledge graph and natural language understanding technology.

[0059] Dynamic update and expansion capabilities: The system can be dynamically updated and expanded to adapt to new regulations and policies and maintain the timeliness and accuracy of the system.

[0060] The above specific implementation methods further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent question-answering method based on natural language processing, characterized in that: The intelligent question-answering method comprises: Collect text data from data sources; Preprocess the collected text data; Analyze the preprocessed text data and extract key information; Build a knowledge graph based on the extracted key information; Use knowledge graphs and natural language understanding technologies to answer user questions.

2. The intelligent question-answering method based on natural language processing according to claim 1, characterized in that: The intelligent question-answering method also includes: providing a user-friendly interface and supporting multiple interaction modes.

3. The intelligent question-answering method based on natural language processing according to claim 1, characterized in that: The text data collected from the data source specifically includes: review reports and access data.

4. The intelligent question-answering method based on natural language processing according to claim 1, characterized in that: The preprocessing of the collected text data specifically includes: performing cleaning, word segmentation, and tagging preprocessing operations on the collected text data.

5. The intelligent question-answering method based on natural language processing according to claim 1, characterized in that: The analysis of the preprocessed text data specifically includes: named entity recognition, syntactic analysis, and sentiment analysis.

6. The intelligent question-answering method based on natural language processing according to claim 1, characterized in that: The constructing of a knowledge graph based on the extracted key information specifically includes: constructing a knowledge graph of a field based on the extracted key information, displaying a variety of entities and relationships.

7. The intelligent question-answering method based on natural language processing according to claim 1, characterized in that: The use of knowledge graph and natural language understanding technology to answer user questions specifically includes: Based on knowledge graph and natural language understanding technology, an intelligent question-answering system is built that can accurately understand user questions and provide corresponding answers.

8. The intelligent question-answering method based on natural language processing according to claim 2, characterized in that: The supporting multiple interaction modes specifically include: text and language.

9. An intelligent question-answering system based on natural language processing, applying an intelligent question-answering method based on natural language processing as described in any one of claims 1 to 8, characterized in that: The intelligent question answering system comprises: A data collection module, used to collect text data from data sources; A data preprocessing module is used to preprocess the collected text data; Natural language processing module, used to analyze the preprocessed text data and extract key information; Knowledge graph construction module, used to construct a knowledge graph based on the extracted key information; Intelligent question-answering module, used to answer user questions using knowledge graph and natural language understanding technology.

10. The intelligent question-answering system based on natural language processing according to claim 9, characterized in that: The intelligent question-answering system also includes: a user interaction module, which is used to provide a user-friendly interface and support multiple interaction modes.

Images