Discriminative Term Importance Analysis for Chatbot Entity Discovery
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Solution Overview
Problem
Conversational systems face limitations in dynamically suggesting new entity types, requiring upfront specification of intents and entities, and existing methods for entity discovery are time-consuming and require domain expert knowledge, limiting flexibility and scalability.
Innovation Solution
A framework for suggesting new entity types with discriminative term importance analysis, performing part-of-speech filtering, stop-word filtering, and re-ranking based on adjusted importance scores to efficiently identify and label new entities without the need for manual seed sets or extensive training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods for entity discovery are used, then entity types can be identified, but the process is time-consuming and requires domain expert knowledge
Solution Approach 1:
The system performs preliminary action by pre-processing the input sentence to generate importance scores for each token before entity discovery. This preliminary importance analysis prepares the data in advance, allowing the entity discovery process to focus only on high-importance tokens, thereby reducing time consumption while maintaining accuracy.
Solution Approach 2:
The system extracts only the most important tokens from the input sentence based on computed importance scores, rather than processing all tokens equally. This extraction of critical information reduces the search space for entity discovery, significantly decreasing processing time while preserving discovery accuracy.
2Reliability
If manual specification of intents and entities is required, then conversational systems can function, but flexibility and scalability are limited
Solution Approach 1:
The system implements self-service by automatically suggesting new entity types based on analyzed input sentences without requiring manual specification. The conversational system serves itself by autonomously expanding its entity vocabulary through the importance analysis mechanism, thereby improving flexibility and scalability while maintaining reliable functionality.
Solution Approach 2:
The system applies dynamics by making the entity specification process adaptive and dynamic rather than static. The importance analysis mechanism dynamically identifies potential entities based on actual usage patterns in input sentences, allowing the system to flexibly adapt to new domains and contexts without manual reconfiguration.
3Measurement precision
If domain expert knowledge is relied upon for entity discovery, then accurate entity identification can be achieved, but the process becomes complex and less accessible
Solution Approach 1:
The system substitutes the mechanical process of manual domain expert analysis with an automated computational mechanism. The importance analysis algorithm replaces the need for expert manual evaluation, computing token importance scores automatically based on linguistic and contextual features, thereby reducing system complexity while maintaining identification accuracy.
Solution Approach 2:
The system changes parameters by using computable linguistic features and statistical measures to determine token importance, replacing the need for expert judgment. By transforming the entity discovery criterion into quantifiable parameters that can be automatically calculated, the system achieves expert-level accuracy through automated parameter analysis rather than manual expertise.
Data Source
AI summary
A mechanism is provided to implement suggestion of new entity types with discriminative importance analysis. The mechanism obtains a list of predefined intents from a chatbot designer. The mechanism receives an input sentence having a target intent within the list of predefined intents. The mechanism performs intent-specific importance analysis on the input sentence to generate an importance score for each token in the input sentence. The mechanism ranks the tokens in the input sentence by importance score and outputs a token with a highest importance score as a candidate entity type.