Utterance Intent Mining via Syntactic Parsing and Word Grouping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional speech recognition systems face low accuracy in classifying utterances due to the complexity of determining intended meaning from multiple words, leading to difficulties in identifying the correct class or meaning.
Innovation Solution
A speech processing system that utilizes a syntactic parser, word extractor, and analyzer to identify syntactic relationships, create word groupings, and map these groupings to candidate intent values, selecting the most likely intended meaning based on frequency analysis of previous utterances, and tags the utterance with appropriate labels to represent its dominant subject matter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition systems analyze utterances for presence of words indicating likely class, then the system can perform basic classification, but the accuracy of classification is quite low
Solution Approach 1:
The utterance is segmented into multiple word groupings based on syntactic relationships. Each grouping represents a potential intended meaning and is analyzed separately. The system divides the classification task into multiple candidate intents, each derived from specific word combinations, rather than treating the utterance as a single unit.
Solution Approach 2:
The system performs partial classification by generating multiple candidate intents from different word groupings rather than attempting to classify the entire utterance at once. Each word grouping is independently evaluated to produce candidate intents, and the final classification is determined by selecting from these partial results based on statistical frequency.
2Measurement precision
If the system creates multiple word groupings to represent different intended meanings, then classification accuracy improves, but the processing complexity increases
Solution Approach 1:
The utterance is divided into multiple word groupings based on syntactic relationships identified by a parser. Each grouping captures a potential intended meaning, and the system processes these segments independently to generate candidate intents, reducing the complexity of analyzing the entire utterance at once.
Solution Approach 2:
The system performs preliminary syntactic parsing and word grouping before final classification. By pre-identifying syntactic relationships and creating word groupings in advance, the system prepares multiple candidate intents that can be efficiently evaluated using statistical frequency data from previous utterances.
3Measurement precision
If the system uses frequency analysis of previous utterances to select the most likely intended meaning, then classification accuracy improves, but the time required for processing increases
Solution Approach 1:
The system pre-computes and stores statistical frequency data from previous utterances during a training phase. This preliminary action allows the system to quickly retrieve frequency information during real-time classification without performing complex calculations, reducing processing time while maintaining accuracy.
Solution Approach 2:
The system uses feedback from historical data by incorporating frequency information from previously classified utterances. This feedback mechanism allows the system to leverage past patterns to quickly determine the most likely intended meaning, improving accuracy without requiring extensive real-time analysis.
Data Source
AI summary
According to example configurations, a speech processing system can include a syntactic parser, a word extractor, word extraction rules, and an analyzer. The syntactic parser of the speech processing system parses the utterance to identify syntactic relationships amongst words in the utterance. The word extractor utilizes word extraction rules to identify groupings of related words in the utterance that most likely represent an intended meaning of the utterance. The analyzer in the speech processing system maps each set of the sets of words produced by the word extractor to a respective candidate intent value to produce a list of candidate intent values for the utterance. The analyzer is configured to select, from the list of candidate intent values (i.e., possible intended meanings) of the utterance, a particular candidate intent value as being representative of the intent (i.e., intended meaning) of the utterance.