Auto Seed Crawler Adaptation Mechanism for NLP Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Natural language processing (NLP) models face inefficiencies due to the collection of unwanted or irrelevant web pages during web crawling, leading to wasted resources and missed opportunities for valuable pages due to limited crawler seed URLs.
Innovation Solution
An automatic crawler seed adaptation mechanism that adjusts the seed URLs list by evaluating productivity using machine learning models, adding new potential seeds, and leveraging original, extended, and search engine seeds to optimize resource allocation and data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a limited crawler seed URL list is used, then the crawling process is manageable and resources are conserved, but many wanted pages are not crawled and valuable pages are missed
Solution Approach 1:
The system implements feedback by analyzing relations data generated from crawled documents and using this analysis to automatically modify the seed URL list. The auto-seed model evaluates productivity metrics and uses this feedback to iteratively improve the seed list, adding valuable pages that were initially missed while removing unproductive ones.
Solution Approach 2:
The crawler system performs self-service by automatically adapting its own seed URL list without external intervention. The auto-seed model evaluates the productivity of existing seeds and autonomously generates new seed URLs based on analyzed relations data, enabling the system to self-optimize its crawling coverage.
2Measurement precision
If all crawled pages are processed by the NLP model, then comprehensive data analysis is achieved, but valuable system and human resources are wasted on unwanted or irrelevant pages
Solution Approach 1:
The system performs preliminary action by analyzing relations data before full NLP model processing. The auto-seed model evaluates productivity metrics from crawled documents in advance, identifying and filtering out unproductive seeds and irrelevant pages before they consume significant NLP processing resources.
Solution Approach 2:
The system extracts and removes harmful elements by filtering out unwanted or irrelevant pages from the crawling process. The auto-seed model identifies and excludes unproductive seeds from the seed URL list, preventing waste of system resources on processing irrelevant content while maintaining comprehensive analysis of valuable pages.
3Reliability
If the seed URL list is manually curated, then quality control is maintained, but the system cannot automatically adapt to discover new potential useful seeds
Solution Approach 1:
The seed URL list transitions from a static manually-curated list to a dynamic automatically-adapting list. The auto-seed model continuously evaluates productivity metrics and modifies the seed list based on analyzed relations data, enabling the system to automatically discover new potential useful seeds while maintaining quality through data-driven selection criteria.
Solution Approach 2:
The manual mechanical process of seed list curation is replaced with an automated machine learning system. The auto-seed model uses machine learning algorithms to evaluate productivity and generate new seed URLs, substituting human manual work with an adaptive computational system that maintains reliability through objective metrics while enabling automatic discovery.
Data Source
AI summary
A computer-implemented method for automatically adjusting a Uniform Resource Locator (URL) seed list. The method includes crawling for documents based on a seed URL list. The method generates relations data from the documents using a Natural Language Processing (NLP) model. The method analyzes the relations data using an auto-seed model. The method modifies the seed URL list.