Pronoun Resolution via Trigger Word Scoring
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Solution Overview
Problem
Natural language processing systems face challenges in accurately identifying the intent behind sentences due to ambiguities in human speech, particularly with pronouns that can refer to multiple candidate nouns, leading to difficulties in understanding and responding to user commands effectively.
Innovation Solution
A pronoun-based natural language processing system that utilizes a dataset to train neural networks, employing the TRANSFORMER architecture and attention mechanism to score candidate nouns based on trigger words, enabling the system to confidently select the correct noun referred to by a pronoun, thereby improving language processing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NLP methods are used to process pronouns, then the system is simpler and faster, but the accuracy of identifying intended nouns deteriorates due to speech ambiguities
Solution Approach 1:
The patent introduces trigger words as intermediary elements that mediate between pronouns and candidate nouns. The system identifies trigger words associated with pronouns and uses them to score and select the intended noun from multiple candidates, resolving the ambiguity without requiring overly complex system architecture
Solution Approach 2:
The patent replaces traditional rule-based or hand-crafted NLP approaches with neural network models trained on linguistic data. This substitution enables the system to learn complex pronoun-resolution patterns automatically, improving accuracy while managing complexity through data-driven learning rather than explicit programming
2Measurement precision
If neural networks with attention mechanism are employed to resolve pronoun ambiguity, then the accuracy of noun identification improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the pronoun resolution process into distinct stages: identifying the pronoun, finding associated trigger words, generating candidate nouns, scoring candidates using attention mechanisms, and selecting the best match. This segmentation allows the system to apply computational resources efficiently at each stage rather than processing everything uniformly
Solution Approach 2:
The system generates multiple candidate nouns and scores them using attention mechanisms, but only performs full neural network processing on a limited set of high-probability candidates. This partial application of complex processing reduces overall computational burden while maintaining accuracy for the most likely interpretations
Data Source
AI summary
Disclosed herein are various embodiments for pronoun-based natural language processing. An embodiment operates by receiving a plurality of text-based sentences each comprising a plurality of words, and each text-based sentence including a pronoun. A plurality of candidate nouns are identified amongst the plurality of words. A trigger word is identified from the plurality of words, wherein the trigger word is associated with both the pronoun and one of the plurality of candidate nouns. A score for each of the candidate nouns is received based on a relationship with the trigger word. The candidate noun with a highest score is selected as being associated with the pronoun.