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https://issues.apache.org/jira/browse/TIKA-369?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ken Krugler updated TIKA-369:
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Attachment: Surprise and Coincidence.pdf
Attaching another paper from Ted that makes it clearer why the chi-squared
method currently used has problems for small text chunks.
> 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, Surprise and Coincidence.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 log-likelihood ratio (LLR) test works much
> better, which would then make language detection faster due to less text
> needing to be processed.
> 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.
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