Deep Learning Information Extraction Model for Open Domain Text
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Solution Overview
Problem
Natural language processing faces challenges in extracting effective knowledge information from flexible and unpredictable text data, particularly in open information extraction scenarios where rule-based methods require numerous manually set rules and struggle with noise and diverse expressions.
Innovation Solution
A deep learning-based method that uses an annotated corpus to construct an information extraction model, which includes acquiring and standardizing sample sentences and their corresponding information sets, and then training a neural network to generate output sequences for extracting knowledge based on verbs, prepositions, noun attributes, and entity relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based methods are used for information extraction, then extraction can be performed with explicit control rules, but the system requires numerous manually set rules and has low adaptability to diverse natural language expressions
Solution Approach 1:
The patent replaces the mechanical rule-based system with a deep learning neural network model. Instead of manually setting syntactic analysis rules and position-based extraction rules, the system uses trained neural networks (including encoder-decoder architectures and attention mechanisms) to automatically learn extraction patterns from annotated corpus data, thereby eliminating the need for numerous explicit rules while improving adaptability to diverse expressions
Solution Approach 2:
The patent transforms the extraction system from a rule-based parameter configuration approach to a data-driven learning approach. By changing the fundamental parameter representation from manual rules to learned neural network weights and attention distributions, the system achieves better adaptability without increasing rule complexity
2Measurement precision
If rule-based methods with manual calibration are used, then some extraction accuracy can be achieved, but the method requires expert knowledge and cannot handle open domain scenarios effectively
Solution Approach 1:
The patent creates a universal information extraction model that can handle multiple extraction tasks across different domains through a single trained neural network. The model uses annotated corpus data from various domains to learn generalizable patterns, enabling it to function effectively in open domain scenarios without requiring domain-specific rule adjustments
Solution Approach 2:
The system enables self-service extraction by training the neural network on annotated data to automatically learn extraction patterns without requiring continuous expert intervention for rule creation. The model serves itself by generalizing from training data to handle new, unseen extraction scenarios in open domains
3Productivity
If syntactic analysis with position rules is used, then structured information can be extracted from sentences, but the method fails to capture semantic relationships and struggles with noise in natural language
Solution Approach 1:
The patent replaces syntactic analysis with neural network-based semantic analysis. Instead of relying on rigid syntactic parsing and position-based rules, the system uses trained neural networks to directly process raw text and capture semantic relationships, making the extraction process more robust to syntactic variations and noise while maintaining efficiency
Data Source
AI summary
A method, apparatus and device are for extracting information. The method includes: acquiring an annotated corpus, which includes a plurality of sample sentences and annotated information sets corresponding to the sample sentences, constructing an input sequence and an output sequence based on the sample sentences and the annotated information sets corresponding to the sample sentences, obtaining an information extraction model generating the output sequence from the input sequence by carrying out training with a deep learning method, and inputting a to-be-processed sentence into the information extraction model to extract a knowledge information set included in the to-be-processed sentence. The annotated information set includes information of at least one piece of the following types of knowledge to be extracted from corresponding sample sentences: knowledge based on verbs or prepositions, knowledge based on noun attributes, knowledge of entity description, and knowledge of a relationship between an entity and a concept.