Neural Machine Reading Comprehension for Complex Question Answering
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Solution Overview
Problem
Current computer-implemented linguistics techniques for answering complex questions are not reliable or accurate, as they fail to effectively utilize syntactic, semantic, and discourse analysis, leading to inaccurate concentration on less important words and phrases in lengthy texts.
Innovation Solution
The method involves generating and validating answers using syntactic, semantic, and discourse analysis to train a machine-learning model, which includes self-attention networks to prioritize important words based on syntactic, semantic, and rhetorical relationships, thereby improving the accuracy of complex question answering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing techniques are used to answer complex questions, then the system can process questions, but the accuracy and reliability of answers deteriorate
Solution Approach 1:
The patent segments the text processing into three distinct linguistic analysis layers: syntactic analysis (parsing sentence structure and grammatical relationships), semantic analysis (extracting meaning and relationships between concepts), and discourse analysis (understanding rhetorical relationships and text organization). Each layer processes the text independently and contributes specialized features to the answer generation, preventing any single analysis method from dominating and improving overall answer reliability
Solution Approach 2:
The patent adds a new dimension to text processing by incorporating discourse analysis alongside traditional syntactic and semantic analysis. This creates a multi-dimensional processing framework where text is analyzed from multiple linguistic perspectives simultaneously, enabling the system to capture nuanced relationships that single-dimension approaches miss, thereby improving answer accuracy
2Measurement precision
If traditional text analysis is used, then processing is simpler, but important words and phrases are not properly prioritized in lengthy texts
Solution Approach 1:
The patent divides the complex task of identifying important words into three separable linguistic analysis components: syntactic analysis identifies grammatical importance, semantic analysis identifies conceptual importance, and discourse analysis identifies rhetorical importance. Each component generates independent importance scores that are integrated to produce a comprehensive word importance ranking, making the complex process manageable and accurate
Solution Approach 2:
The patent applies multiple layers of linguistic analysis (syntactic, semantic, and discourse) beyond what traditional single-layer analysis provides. This excessive action of analyzing the text from multiple perspectives ensures that important words are not missed, with each analysis layer compensating for limitations in the others, thereby improving word importance detection accuracy
3Measurement precision
If comprehensive linguistic analysis is applied, then answer accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the comprehensive linguistic analysis into three independent but complementary modules: syntactic analysis module, semantic analysis module, and discourse analysis module. Each module processes text independently and contributes specific features to the answer generation, allowing the system to achieve high accuracy through combined analysis while managing computational complexity through modular architecture
Solution Approach 2:
The patent creates a universal machine learning model that integrates multiple linguistic analysis functions into a single system. The model simultaneously processes syntactic, semantic, and discourse features to generate answers, making the system multi-functional while avoiding the need for separate models for each analysis type, thereby balancing accuracy improvement with complexity management
Data Source
AI summary
An autonomous agent receives a user query comprising the complex question. The agent can obtain, from a corpus of unstructured texts, an answer candidate text corresponding to the user query and comprising text from which the answer is subsequently identified. The agent may generate first linguistic data corresponding to the user query and second linguistic data corresponding to the answer candidate text. Each instance of linguistic data may comprise a combination of respective syntactic data, semantic data, and discourse data generated from the user query and/or answer candidate text. Both instances of linguistic data may be provided to a machine-learning model that has been previously trained to output an answer identified from an instance of unstructured text (e.g., the answer candidate text). The model may output the answer identified from the answer candidate text, which in turn may be provided in response to the user query.