Skip to content
Using supervised learning, create a set of affix rules for use by the CSTlemma lemmatiser.
C++ Other
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.
doc
example
src
COPYING
Changelog
README.md

README.md

affixtrain

Using supervised learning, create a set of affix rules for use by the CSTlemma lemmatiser.

Training takes place in three stages:

  • In the first stage the program tries to find a set of six parameters that optimize the tree produced for the current language. In practice, the best way of optimization is finding a (local) minimum for the number of tree nodes. Other optimizations can give marginally better results.
  • In the second stage the program trains trees for increasing numbers of training examples, starting with a very small percentage and ending with almost all available training examples. The remaining examples are used for testing. Depending on the size of the training or test set, the procedure is repeated for different random samples. For each training set size the precision is estimated, together with standard deviation. The parameters are computed for the power law that gives the expected number of rules for a given number of training words. The lower the exponent, the better is the generalization effect. For each training set size the rules are also pruned a number of times. For each pruning level the precision is computed anew. Often, the best rules (fewest wrongly lemmatised words) are obtained by pruning the rules that are supported by only one or two examples. A tabular report is added (as a comment) to a parameter file that can be used for future reference and re-training. You can also test the rules with the training data. The unpruned rules may not produce any wrong results. (If they do, tell me!)
  • In the third stage all available training words are used to produce production-ready rules, again in several pruning levels. It is up to the user to decide the pruning level to use. Unpruned rules lemmatise all training words correctly, but may give more erroneous lemmas for out-of- vocabulary words. Pruned rules may give better results for OOV words, but will not lemmatize all training words correctly. To cope with this disadvantage you should also provide a binary dictionary to the lemmatiser. Use cstlemma -h to see how to do that.
  • To test that lemmatisation takes place as intended ("unit test") you can provide an already generated flexrule file and a list of words, which is then lemmatised. You can also just provide an already generated flexrule file to pretty print it and to convert it to Bracmat format. The latter file can be read and used by the Bracmat script lemmatize.bra.

Empty lines in the training/testing data are interpreted as cluster separators. If the data has no empty lines between non-empty lines, the training and testing occurs on a line-by-line basis, but if there are such empty lines, training and testing occurs on a cluster-by-cluster basis. For example, by collecting homographs in clusters and defining all non-ambiguous full forms as one-line clusters, testing with 'OOV' words (that is, words that were not used during training) will result in more realistic estimates of how well the rules are able to spot and lemmatize ambiguous full forms.

Notice that the whole process easily can take many days, even a couple of weeks, to run.

This version still contains a lot of "dead wood" and confusing naming. We work on that.

Bart Jongejan, April 21, 2015

Something went wrong with that request. Please try again.