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O-Funnel library offers lossless data extraction from evolving documents

By PulseAugur Editorial · [1 sources] ·

Researchers have developed O-Funnel, a new Python library designed to extract data from heterogeneous and evolving documents without manual pattern writing. O-Funnel separates structural capture from value extraction, transcribing various document formats like XML, JSON, and HTML into a unified tree structure. This approach ensures that data extraction remains robust even when schemas change, as demonstrated by its perfect accuracy on PubMed records after a schema rename, outperforming traditional regex-based parsers. AI

IMPACT Enhances data processing capabilities for AI systems dealing with varied and changing data sources.

RANK_REASON Research paper detailing a new method and library for data extraction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

O-Funnel library offers lossless data extraction from evolving documents

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Research paper detailing a new method and library for data extraction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Osama Mustafa ·

    O-Funnel: Lossless Structural Capture and Requirement-Driven Extraction from Drifting, Heterogeneous Documents

    Pulling a fixed set of fields out of documents that arrive in many formats and under drifting schemas is usually done with hand-written byte patterns, which break whenever a key is renamed, a value is reformatted, or a lookalike value appears first. We argue the cause is structur…

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