Information Query via Dependency Parsing and Template Matching
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Solution Overview
Problem
Existing information query methods face challenges in ensuring accuracy and efficiency due to the richness of language expressions, leading to high costs and low efficiency in processing and retrieving relevant data, particularly in natural language processing and deep learning applications.
Innovation Solution
A method and apparatus for information query that utilizes dependency parsing to segment query sentences into word segments and obtain their part of speech, generating a coding sequence which is then matched with generalized templates to extract core corpuses, thereby obtaining query results without relying on massive tagged data, enhancing generalization ability and universality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional information query methods are used to process natural language queries, then the system can handle diverse language expressions, but the processing cost increases and efficiency decreases
Solution Approach 1:
The query sentence is segmented into word segments through dependency parsing, breaking down the natural language query into structured components. This segmentation allows the system to process each component independently according to its grammatical role, improving efficiency while maintaining the ability to handle diverse expressions.
Solution Approach 2:
The system transforms the natural language query into a coding sequence by changing the parameter representation from raw text to structured grammatical codes. This parameter transformation enables efficient matching with generalized templates while preserving the semantic meaning of diverse language expressions.
2Measurement precision
If massive tagged data is used for training query models, then the accuracy of query results improves, but the cost of data preparation and processing increases
Solution Approach 1:
The generalized templates serve as universal patterns that can match multiple different query expressions. Instead of requiring separate training data for each expression variant, the system uses these templates to generalize across diverse queries, reducing the need for massive tagged data while maintaining accuracy.
Solution Approach 2:
The system extracts the core semantic structure from queries through dependency parsing and part-of-speech analysis, separating the essential grammatical components from the specific wording. This extraction allows the system to work with structured coding sequences rather than requiring extensive tagged training data for each linguistic variation.
3Adaptability or versatility
If dependency parsing and generalized template matching are used, then the system achieves high generalization ability, but the complexity of the processing method increases
Solution Approach 1:
The system performs preliminary dependency parsing and part-of-speech tagging to generate coding sequences before the actual template matching process. This preliminary structuring of the query simplifies the subsequent matching operation, as the system only needs to compare pre-processed coding sequences against templates rather than analyzing raw text in real-time.
Data Source
AI summary
The present application discloses a method and an apparatus for information query, and an electronic device, which relates to a field of deep learning (DL), natural language processing (NLP) and artificial intelligence (AI) technology. The method includes: receiving a query sentence, segmenting the query sentence to obtain word segments, and obtaining a dependency relationship between two word segments and part of speech of the word segments; obtaining a coding sequence of the query sentence according to the dependency relationship and the part of speech of the word segments; matching the coding sequence with a generalized template to obtain a core corpus of the query sentence, wherein the generalized template comprises part of speech to be extracted and a dependency relationship to be extracted; and obtaining a query result corresponding to the query sentence based on the core corpus. The application no longer relies on the accumulation of massive business scenario data to enhance a generalization ability, which ensures accurate and efficient information query, and improves the efficiency and reliability of the information query process. At the same time, it may support information query in different business scenarios, with strong expansion capability and high universality.