Multilingual Webpage Filtering for Topic-Specific AI Training Data
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Solution Overview
Problem
Existing artificial intelligence systems face challenges in collecting high-quality, domain-specific, and language-specific training data efficiently, leading to costly and time-consuming processes, and lack control over data quality and accuracy.
Innovation Solution
A method for obtaining and filtering large text corpora to generate high-quality, topic-specific datasets in multiple languages by annotating, filtering, and post-processing webpages to create training datasets for machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random data collection methods are used, then data collection speed is improved, but data quality and domain-specific accuracy deteriorate
Solution Approach 1:
The system performs preliminary classification of webpages into topic categories before filtering. By pre-organizing the large text corpus into structured topic hierarchies with classification labels, the system enables efficient subsequent filtering for domain-specific data while maintaining high collection speed through automated preprocessing.
Solution Approach 2:
The patent introduces an intermediary filtering layer between random data collection and final dataset generation. Multiple filtering stages (topic filtering, language filtering, domain-specific filtering) act as intermediaries to progressively refine raw collected data into high-quality domain-specific training datasets.
2Manufacturing precision
If domain-specific filtering is applied, then data accuracy is improved, but processing time increases
Solution Approach 1:
The system segments the filtering process into multiple independent stages: topic-based filtering, language filtering, and domain-specific filtering. Each stage processes data independently with optimized criteria, allowing parallel execution and reducing overall processing time while maintaining high accuracy through cumulative filtering effects.
Solution Approach 2:
The system performs preliminary topic classification and language identification before domain-specific filtering. By pre-organizing data with classification labels and language tags, the system reduces the computational burden of subsequent domain filtering operations, enabling faster processing without sacrificing accuracy.
3Adaptability or versatility
If multiple language datasets are collected, then model versatility is improved, but data collection complexity increases
Solution Approach 1:
The system implements a universal data collection framework that simultaneously handles multiple languages through a single integrated pipeline. The classification and filtering mechanisms are language-agnostic, applying the same topic-based and domain-specific criteria across all languages, which simplifies the collection process while enabling multi-language model training.
Solution Approach 2:
The system performs preliminary language identification and tagging for all collected webpages before domain filtering. By pre-marking language attributes in the classification labels, the system enables efficient subsequent filtering for specific languages without adding complexity to the core domain filtering logic, thus supporting multi-language versatility with manageable complexity.
Data Source
AI summary
Obtaining collections of sentences in different languages that are usable for training models in various applications of artificial intelligence is provided. A method is provided that obtains, from text corpus, webpages in a plurality of languages, each of the webpages corresponding to an URL; obtains annotations for each of the webpages based on its URL, to obtain annotated data entries corresponding to the webpages, each of the annotated data entries including a classification label corresponding to a sub-topic of one of a plurality of topics, where each of the plurality of topics includes a corresponding plurality of sub-topics; filters the annotated data entries to obtain topic-specific content in a target language based on the classification labels, the topic-specific content corresponding to one or more sub-topics; performs post-processing on the topic-specific content to obtain result data; and outputs the result data for the topic.