Multi-Word Concept Tokenization for Accurate Part-of-Speech Tagging
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Solution Overview
Problem
Current natural language processing systems face challenges in accurately identifying parts of speech for complex topics described by multiple words, requiring time-consuming and resource-intensive training, and struggle with multi-word concepts, leading to incorrect tagging and increased computational demands.
Innovation Solution
A system that identifies multi-word concepts as tokens and replaces them with desired parts of speech, allowing a part-of-speech tagger to accurately tag the remaining text, without the need for training, thereby reducing computational resources and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current natural language processing systems use traditional part-of-speech taggers to identify parts of speech for complex topics described by multiple words, then the systems can process text, but they require time-consuming and resource-intensive training and struggle with multi-word concepts leading to incorrect tagging
Solution Approach 1:
The system performs preliminary action by identifying and replacing multi-word concepts with tokens before the part-of-speech tagging process. This preprocessing step ensures that complex multi-word expressions are handled as unified units, eliminating the need for time-consuming training while improving tagging accuracy for complex topics.
Solution Approach 2:
The system introduces tokens as intermediary elements that represent multi-word concepts. These tokens serve as mediators between the original complex text and the part-of-speech tagger, allowing the tagger to process simplified representations while maintaining accuracy for complex expressions without requiring extensive training.
2Measurement precision
If current natural language processing systems use traditional part-of-speech taggers to identify parts of speech for complex topics described by multiple words, then the systems can process text, but they require resource-intensive training and increased computational demands
Solution Approach 1:
The system performs preliminary action by identifying and replacing multi-word concepts with tokens before the part-of-speech tagging process. This preprocessing step simplifies the input for the tagger, reducing computational complexity and resource requirements while maintaining or improving tagging accuracy for complex topics.
Solution Approach 2:
The system segments the text processing into distinct stages: first identifying and replacing multi-word concepts with tokens, then performing part-of-speech tagging on the simplified text. This segmentation reduces the computational burden on each individual processing stage, lowering overall resource consumption while improving accuracy.
3Adaptability or versatility
If current natural language processing systems process complex multi-word concepts using traditional methods, then they can handle various text types, but they experience incorrect tagging and increased computational demands
Solution Approach 1:
The system performs preliminary action by identifying and replacing multi-word concepts with tokens before part-of-speech tagging. This ensures that versatile text processing capabilities are maintained while reliability of tagging is improved, as complex multi-word expressions are handled as unified units from the outset.
Solution Approach 2:
The system introduces tokens as intermediary elements that represent multi-word concepts. These tokens maintain the semantic integrity of complex expressions while providing a simplified interface for the part-of-speech tagger, thereby improving tagging reliability without limiting text processing versatility.
Data Source
AI summary
A system for providing intelligent part of speech processing of complex natural language is disclosed. The system identifies a multiword concept from an input and replaces the multiword concept with a token to be tagged as a desired part of speech. The system passes the modified text including the token to a part-of-speech tagger to tag each word in the text with the appropriate part-of-speech. The system may replace the token with the original text that the token was utilized to replace so that the original intent of the text is evident. The system may analyze the tagged text to generate analyses and interpretations associated with the input. When multiple multiword concepts are identified, the system may evaluate them by computing scores for each of the multiword concepts that may be replaced with tokens, for each of the modified texts including the tokens, or for any interpretations and analyses thereof.