📄 Uncertainty Detection for Information Extraction

Summary

Caveat: the supplied PDF extraction is unreadable. The font encoding came through as scrambled glyphs, so no sentence of the body can be recovered. This entry rests only on the title, author, venue and page range, and makes no claims about the paper's data, method or numbers. Judging by the title, Bénédicte Goujon's RANLP-2009 paper addresses a gap in information extraction (IE). Extracted facts are usually stored as if the source asserted them as certain, even when the text hedges, reports rumours or speculates. The paper's aim is to detect expressions of uncertainty so that extracted items can carry that information. The extraction does not let us confirm the language studied, the detection technique (rules, lexicons or learning), the corpus or the evaluation scores. This matters because downstream users of IE output, such as analysts or knowledge bases, need to tell stated facts from conjecture. Before anyone cites specifics, a librarian should re-extract the PDF (OCR or a different text extractor) and revise this entry.

Key points

• Bibliographic data: Goujon, Bénédicte, "Uncertainty Detection for Information Extraction", Proceedings of RANLP-2009, Borovets, Bulgaria, pages 118–122.
• Topic, from the title: identifying uncertainty in text as part of an information-extraction pipeline.
• The extracted text is fully glyph-encoded, so the method, corpus, resources and language cannot be verified from this copy.
• Page 5 seems to contain a table of decimal scores and a reference list, but the digits are encoded and cannot be read reliably. No result numbers are reported here for that reason.
• Action item: re-extract the PDF with OCR and update this summary.

Interesting ideas

• I1. Based on the title only: record each extracted fact together with its certainty status instead of flattening everything to "true", so that hedged statements stay separate from asserted ones.
• I2. Based on the title only: treat uncertainty detection as its own step that annotates extraction output, rather than folding it into the extractor.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

• I1 (world): Entities, states and events in the world of the text could carry a certainty attribute (asserted, hedged, reported). The answer stage could then explain that something "is claimed" rather than "is".
• I2 (frames): The prag crate could add an explainable certainty layer over frames, attached to the UD subtree that triggers it. Speech-act and implicature handling could reuse this layer.