dimus
commented
1 year ago Thank you for your feedback @Teinostoma. Currently there is a hybrid approach to BHL names. There are name-finding methods that used ngram approach and BHLindex. BHLindex creates very few false positives, but misses many names with OCR errors. Names found before BHLindex using ngram did find many names with OCR errors, however they also contain a lot of false positives. We are thinking about ways to fix this problem.
Do you know a good resource for fossil mollusks names? Currently, I think, there are 3 sources of mollusks names: WoRMS, PaleoBioDB, and for old names -- Sherborn's Index Animalium.
Context, usage of dates, figuring out how to reconcile abbreviated names -- definitely a way to improve name-fidning. I am actually writing a grant exacly about that.
Implementing user-feedback for names would be out of scope of BHLindex, and more of a decision for BHL folks (@mlichtenberg, @cajunjoel). @gdower also might be interested.
Teinostoma
OCR often does very poorly on documents in BHL, and the list of names being searched for is very incomplete, at least when it comes to fossil mollusks. Authors also did not make this easy, often using idiosyncratic ways of abbreviating. As a result, both the false positive and false negative rates are very high in the documents that I am reading on BHL. A few ideas:
Is there a way to take the date of the publication into consideration? Names published after a publication was written will not be found in that publication (for example, the word lens will not be a reference to the genus Lens Simpson, 1900 in publications from the 1800's). This would help decease false positives.
Is there a way to allow users to quickly indicate "here is a name missed by the system", "this is correct", "this name finding is spurious", etc.? It would require verification to protect against trolling or errors, but could be a useful way to improve the name finding.
Is there a way to take context into account to identify higher taxonomic levels? This is especially of value for homonyms. For example, being able to search for references that contain both Auricularia and Mollusca would avoid the huge number of hits for the fungus Auricularia.