Once your dictionaries are of a reasonable size, say perhaps 3,000 lemmata in total, it is worth training the tagger. To do this, you'll need a couple of things, a decent sized corpus, either tagged or untagged, and a
.tsx file. The basic instructions may be found below.
Creating a corpus
A basic corpus can be retrieved from a Wikipedia dump (see here) as follows:
$ bzcat afwiki-20070508-pages-articles.xml.bz2 | grep '^[A-Z]' | sed 's/$/\n/g' | sed 's/\[\[.*|//g' | sed 's/\]\]//g' | sed 's/\[\[//g' | sed 's/&.*;/ /g' > mycorpus.txt
Another option for stripping Wikipedia, which will probably result in a higher quality corpus, is as follows. First download the Wikipedia extractor script from here, then:
$ bzcat enwiki-20081008-pages-articles.xml.bz2.part > enwiki.xml
Now use the script above to get
enwiki.txt and then
$ cat enwiki-20091001-pages-articles.txt | grep -v "''" | grep -v http | grep -v "#" | grep -v "@" |\ grep -e '................................................' | sort -fiu | sort -R | nl -s ". " > enwiki.crp.txt
The last 3 commands are not strictly necesary. They sort and finds only uniqe lines, then sorts randomly (mix the sentences) and adds line numbers.
Writing a TSX file
- See also: TSX format
.tsx file is a tag definition file, it turns the fine tags from the morphological analyser into coarse tags for the tagger. The DTD is in
tagger.dtd, although it is probably easier to take a look at one of the pre-written ones in other language pairs.
The file should be in the language pair directory and be called (in for example English-Afrikaans),
apertium-en-af.en.tsx for the English tagger, and
apertium-en-af.af.tsx for the Afrikaans tagger.
The TSX file defines a set of "coarse tags" for groups of "fine tags", this is done because the POS tagging module does not need so much information as is defined in the fine tags. It also allows the user to apply a set of restrictions or enforcements. For example to forbid a relative adverb at the start of a sentence (
SENT RELADV), or to forbid a pronoun after a noun (
You can also write lexical rules, so for example in Afrikaans, the word "deur" is ambiguous, one meaning is "by" (as a preposition) and the other is "door" (as a noun). So we can define two coarse tags,
DEURPR, and then a forbid rule to say "forbid 'door' before 'the'".
It is worth considering this file carefully and probably also consulting with a linguist, as the tagger can make a big difference to the quality of the final translation. The example below gives the basic structure of the file:
<?xml version="1.0" encoding="UTF-8"?> <tagger name="afrikaans"> <tagset> <def-label name="DEURNOM" closed="true"> <tags-item lemma="deur" tags="n.*"/> </def-label> <def-label name="DEURPR" closed="true"> <tags-item lemma="deur" tags="pr"/> </def-label> <def-label name="NOM"> <tags-item tags="n.*"/> </def-label> <def-label name="PRNPERS" closed="true"> <tags-item tags="prpers.*"/> </def-label> <def-label name="DET" closed="true"> <tags-item tags="det.*"/> </def-label> </tagset> <forbid> <label-sequence> <label-item label="NOM"/> <label-item label="PRNPERS"/> </label-sequence> <label-sequence> <label-item label="DEURNOM"/> <label-item label="DET"/> </label-sequence> </forbid> </tagger>
You will need enough coarse tags to cover all the fine tags in your dictionaries.
Training the tagger
A brief note on the various kinds of training that you can do:
- Unsupervised — This uses a large (hundreds of thousands of words) untagged corpus and the iterative Baum-Welch algorithm in a wholely unsupervised manner. This is the least effective way of training the tagger, but is also the cheapest in terms of time and resources.
- Supervised — This uses a medium sized (minimum 30,000 words) tagged corpus.
apertium-tagger-trainer— This uses a large untagged corpus in the target language, a previously trained
.probfile and an existing translator. It performs as well as supervised training without the need of hand-tagging a corpus, at the expense of being a bit tricky to set up.
At the moment
apertium-tagger-trainer only works with apertium 1, so it's not an option for most pairs.--Jacob Nordfalk 06:15, 17 September 2008 (UTC)
(Clarification: it only works with one-stage transfer, so Apertium 3 pairs which only have t1x can still use it.)
- Main article: Unsupervised tagger training
- Main article: Supervised tagger training
Target language tagger training
- Main article: Target language tagger training
There is a package called
apertium-tagger-training-tools that trains taggers based on both source and target language information. The resulting probability files are as good as supervised training for machine translation purposes, but much quicker to produce, and with less effort.
- (bibtex) Felipe Sánchez-Martínez, Juan Antonio Pérez-Ortiz, Mikel L. Forcada (2008). "Using target-language information to train part-of-speech taggers for machine translation". In Machine Translation, volume 22, numbers 1-2, p. 29-66.
- (bibtex) Felipe Sánchez-Martínez (2008). "Using unsupervised corpus-based methods to build rule-based machine translation systems". PhD thesis, Departament de Llenguatges i Sistemes Infomàtics, Universitat d'Alacant, Spain.
- Felipe Sánchez-Martínez, Carme Armentano-Oller, Juan Antonio Pérez-Ortiz, Mikel L. Forcada. (2007) "Training part-of-speech taggers to build machine translation systems for less-resourced language pairs". Procesamiento del Lenguaje Natural nº 39, (XXIII Congreso de la Sociedad Española de Procesamiento del Lenguaje Natural), pp. 257—264
- Felipe Sánchez-Martínez, Juan Antonio Pérez-Ortiz, Mikel L. Forcada. (2004) "Cooperative unsupervised training of the part-of-speech taggers in a bidirectional machine translation system". Proceedings of TMI, The Tenth Conference on Theoretical and Methodological Issues in Machine Translation, pp. 135—144