[
https://issues.apache.org/jira/browse/TIKA-369?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ken Krugler updated TIKA-369:
-----------------------------
Attachment: lingdet-mccs.pdf
Smaller version of Ted Dunning's 1994 paper.
> Improve accuracy of language detection
> --------------------------------------
>
> Key: TIKA-369
> URL: https://issues.apache.org/jira/browse/TIKA-369
> Project: Tika
> Issue Type: Improvement
> Components: languageidentifier
> Affects Versions: 0.6
> Reporter: Ken Krugler
> Assignee: Ken Krugler
> Attachments: lingdet-mccs.pdf
>
>
> Currently the LanguageProfile code uses 3-grams to find the best language
> profile using Pearson's chi-square test. This has three issues:
> 1. The results aren't very good for short runs of text. Ted Dunning's paper
> (attached) indicates that a Lucas-Lehmer-Riesel (LLR) test works much better,
> which would then make language detection faster due to less text needing to
> be processed. It might be sufficient to re-enable support for 1..4-grams
> (similar to original Nutch code) to improve quality.
> 2. The current LanguageIdentifier.isReasonablyCertain() method uses an exact
> value as a threshold for certainty. This is very sensitive to the amount of
> text being processed, and thus gives false negative results for short runs of
> text.
> 3. Certainty should also be based on how much better the result is for
> language X, compared to the next best language. If two languages both had
> identical sum-of-squares values, and this value was below the threshold, then
> the result is still not very certain.
--
This message is automatically generated by JIRA.
-
You can reply to this email to add a comment to the issue online.
