Cross-Domain Text Classification via Source Clustering
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Solution Overview
Problem
Conventional machine learning-based automation systems for cross-domain text classification face challenges due to dissimilar data distributions and disparate label sets across different source domains, making it cumbersome to implement effective cross-domain classification.
Innovation Solution
A method and system that generate clusters from text segments associated with source domains, compute similarity scores for target domain text segments, and identify a pre-specified count of clusters to train a classifier for automatic text classification, enabling efficient cross-domain classification regardless of data distribution and label set disparities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional supervised machine learning systems are used for cross-domain text classification, then labeled examples from source domains can be utilized, but the process becomes cumbersome due to dissimilar data distributions and disparate label sets
Solution Approach 1:
The patent segments the source domain data into multiple clusters based on semantic similarity, where each cluster represents a coherent topic or category. This segmentation allows the system to handle dissimilar data distributions by organizing them into manageable groups, reducing implementation complexity while maintaining cross-domain adaptability
Solution Approach 2:
The patent introduces an intermediary clustering mechanism that acts as a bridge between source domains with different label sets and the target domain. By computing similarity scores between target text segments and cluster centroids, the system mediates the transfer of knowledge across domains with disparate label sets, simplifying the overall classification process
2Quantity of substance
If multiple source domains with dissimilar data distributions are used for training, then more diverse knowledge can be leveraged, but the difficulty of detecting and measuring similarity increases
Solution Approach 1:
The patent transforms the similarity measurement problem by changing the parameter space from direct text segment comparison to cluster centroid comparison. By representing each cluster with a centroid (aggregate representation) and computing similarity between target segments and centroids, the system simplifies the measurement process while incorporating diverse knowledge from multiple source domains with different data distributions
3Measurement precision
If manual labeling of training data is performed, then classification accuracy can be improved, but the time and resources required increase significantly
Solution Approach 1:
The patent performs preliminary clustering of source domain text segments based on their semantic similarity before the actual classification task. This preliminary action organizes the data into coherent groups, allowing the system to automatically identify relevant training examples for the target domain without manual labeling, thereby maintaining classification accuracy while eliminating time-consuming manual annotation
Solution Approach 2:
The system performs self-service by automatically selecting and weighting training examples from source domains based on cluster similarity scores. Instead of requiring manual identification of relevant training data, the algorithm autonomously determines which clustered segments are most similar to the target domain and uses them for training, significantly reducing manual intervention while preserving accuracy
Data Source
AI summary
The disclosed embodiments illustrate methods of data processing for text classification of a target domain. The method includes generating a plurality of clusters from a plurality of first text segments corresponding to a plurality of source domains, based on an association of the plurality of first text segments with a plurality of categories. The method further includes computing a similarity score of each of a plurality of second text segments corresponding to the target domain for each of the plurality of clusters. The method further includes identifying a pre-specified count of clusters from the plurality of clusters, based on the computed similarity score. Further, the method includes training a first classifier by utilizing first text segments in the identified pre-specified count of clusters, wherein the trained first classifier is utilized to automatically classify the plurality of second text segments into categories associated with the identified pre-specified count of clusters.