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This is a PHP port of the TextCat language guesser utility.
Please see also the original Perl version, and an updated Perl version.
The package contains the classifier class itself and two tools—for classifying the texts and for generating the ngram database. The code now assumes the text encoding is UTF-8, since it's easier to extract ngrams this way. Also, everybody uses UTF-8 now and I, for one, welcome our new UTF-8-encoded overlords.
The classifier is the script catus.php, which can be run as:
echo "Bonjour tout le monde, ceci est un texte en français" | php catus.php -d LM
or
php catus.php -d LM -l "Bonjour tout le monde, ceci est un texte en français"
The output would be the list of the languages, e.g.:
fr OR ro
Please note that the provided collection of language models includes a
model for Oriya (ଓଡ଼ିଆ), which has the language code or, so results
like or OR sco OR ro OR nl are possible.
To generate the language model database from a set of texts, use the
script felis.php. It can be run as:
php felis.php INPUTDIR OUTPUTDIR
And will read texts from INPUTDIR and generate ngrams files in
OUTPUTDIR. The files in INPUTDIR are assumed to have names like
LANGUAGE.txt, e.g. english.txt, german.txt, klingon.txt, etc.
If you are working with sizable corpora (e.g., millions of characters),
you should set $minFreq in TextCat.php to a reasonably small value,
like 10, to trim the very long tail of infrequent ngrams before they
are sorted. This reduces the CPU and memory requirements for generating
the language models. When evaluating texts, $minFreq should be set
back to 0 unless your input texts are fairly large.
The package comes with a default language model database in the LM
directory and a query-based language model database in the LM-query
directory. However, model performance will depend a lot on the text
corpus it will be applied to, as well as specific modifications—e.g.
capitalization, diacritics, etc. Currently the library does not modify
or normalize either training texts or classified texts in any way, so
usage of custom language models may be more appropriate for specific
applications.
Model names use Wikipedia language codes, which are often but not guaranteed to be the same as ISO 639 language codes.
When detecting languages, you will generally get better results when you
can limit the number of language models in use, especially with very
short texts. For example, if there is virtually no chance that your text
could be in Irish Gaelic, including the Irish Gaelic language model
(ga) only increases the likelihood of mis-identification. This is
particularly true for closely related languages (e.g., the Romance
languages, or English/en and Scots/sco).
Limiting the number of language models used also generally improves
performance. You can copy your desired language models into a new
directory (and use -d with catus.php) or specify your desired
languages on the command line (use -c with catus.php).
You can also combine models in multiple directories (e.g., to use the
query-based models with a fallback to Wiki-Text-based models) with a
comma-separated list of directories (use -d with catus.php).
Directories are scanned in order, and only the first model found with a
particular name will be used.
The 70 language models in LM are based on text extracted from randomly
chosen articles from the Wikipedia for that language. The languages
included were chosen based on a number of criteria, including the number
of native speakers of the language, the number of queries to the various
wiki projects in the language (not just Wikipedia), the list of
languages supported by the original TextCat, and the size of Wikipedia
in the language (i.e., the size of the collection from which to draw a
training corpus).
The training corpus for each language was originally made up of ~2.7 to
~2.8M million characters, excluding markup. The texts were then lightly
preprocessed. Preprocessing steps taken include: HTML Tags were removed.
Lines were sorted and uniq-ed (so that Wikipedia idiosyncrasies—like
"References", "See Also", and "This article is a stub"—are not
over-represented, and so that articles randomly selected more than once
were reduced to one copy). For corpora in Latin character sets, lines
containing no Latin characters were removed. For corpora in non-Latin
character sets, lines containing only Latin characters, numbers, and
punctuation were removed. This character-set-based filtering removed
from dozens to thousands of lines from the various corpora. For corpora
in multiple character sets (e.g., Serbo-Croatian/sh, Serbian/sr,
Turkmen/tk), no such character-set-based filtering was done. The final
size of the training corpora ranged from ~1.8M to ~2.8M characters.
These models have not been thoroughly tested and are provided as-is. We may add new models or remove poorly-performing models in the future.
These models have 10,000 ngrams. The best number of ngrams to use for
language identification is application-dependent. For larger texts
(e.g., containing hundreds of words per sample), significantly smaller
ngram sets may be best. You can set the number to be used by changing
$maxNgrams in TextCat.php or in felis.php, or using -m with
catus.php.
The 30 language models in LM-query are based on query data from
Wikipedia which is less formal (e.g., fewer diacritics are used in
languages that have them) and has a different distribution of words than
general text. The original set of languages considered was based on the
number of queries across all wiki projects for a particular week. The
text has been preprocessed and many queries were removed from the
training sets according to a process similar to that used on the
Wiki-Text models above.
In general, query data is much messier than Wiki-Text—including junk text and queries in unexpected languages—but the overall performance on query strings, at least for English Wikipedia—is better.
The final set of models provided is based in part on their performance on English Wikipedia queries (the first target for language ID using TextCat). For more details see our [initial report](https://www.mediawiki.org/wiki/User:TJones_(WMF)/Notes/ Language_Detection_with_TextCat) on TextCat. More languages will be added in the future based on additional performance evaluations.
These models have 10,000 ngrams. The best number of ngrams to use for
language identification is application-dependent. For larger texts
(e.g., containing hundreds of words per sample), significantly smaller
ngram sets may be best. For short query seen on English Wikipedia
strings, a model size of 3000 to 9000 ngrams has worked best, depending
on other parameter settings. You can set the number to be used by
changing $maxNgrams in TextCat.php or in felis.php, or using -m
with catus.php.
