Multi-Party Dialogue Concept Linking for Faster Domain Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional natural language processing technologies struggle to accurately understand and process multi-entity dialogue data, leading to inefficiencies in computing resources and time-intensive querying due to the ambiguity and complexity of dynamic conversations, especially in domains like biomedicine.
Innovation Solution
A system and method for enhancing natural language processing by identifying target concepts and dialogue goals in multi-entity dialogue data, linking them to structured links, which allows for efficient extraction and storage of relevant concepts, reducing the need to store entire dialogue data and improving query response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If entire dialogue data is stored for later querying, then complete information is preserved, but computing resources and storage space are consumed inefficiently
Solution Approach 1:
The patent extracts and stores only the identified target concepts and dialogue goals from the full dialogue data, rather than storing the entire dialogue. This extraction approach preserves the essential information needed for queries while significantly reducing storage requirements and improving resource efficiency.
2Loss of information
If entire dialogue data is stored for later querying, then complete information is preserved, but query processing time increases
Solution Approach 1:
By extracting and storing only the target concepts and dialogue goals, the system reduces the amount of data that needs to be processed during queries. This extraction creates a condensed index that speeds up query processing while maintaining information completeness for the relevant concepts.
Solution Approach 2:
The system performs preliminary processing of the dialogue data to identify and extract target concepts and dialogue goals before queries are received. This preliminary action creates a pre-processed structure that enables faster query responses without requiring full re-processing of the entire dialogue data.
3Productivity
If conventional NLP tools are used, then processing is attempted, but accuracy in extracting relevant concepts is insufficient
Solution Approach 1:
The system uses a machine learning model that receives feedback from the NLP processing results to improve its accuracy. The model learns from the dialogue data and query patterns to better identify target concepts and dialogue goals, progressively improving concept extraction accuracy while maintaining processing capability.
Data Source
AI summary
Embodiments relate to systems and methods that retrieve dialogue data associated with a plurality of utterances. The plurality of utterances include a first utterance. The systems and methods further determine that a target concept, of the dialogue data, is in a first dialogue segment associated with the first utterance. Additionally, the target concept is determined based on the first utterance in the first dialogue segment having a highest weight for relevancy to a knowledge domain. Further, the methods and systems determine a dialogue goal comprising the target concept. Due to the dialogue goal comprising the target concept, a structured link associating the target concept to the dialogue goal is generated.