One of the problems we tackled here at my university is one as old as the modern Romanian language. It is a problem for linguists, as well as for foreigners trying to learn the language. We call it the root alternations problem.
Similar to French and other languages, Romanian verbs are split into four groups with different conjugation patterns. Except for the irregular verbs, this categorization is performed based on the suffix of the infinitive. However, the conjugation is not straightforward even within these classes, because many verbs exhibit alternations in their root. For example, the verb a purta (to wear) becomes eu port (I wear) but el poartă (he wears). It can be seen that the letter o in the root changes to oa during conjugation. This makes learning the language quite difficult, because we have no rules to describe when these changes occur.
Attempts to formalize such rules from a computer scientific point of view date back to G. C. Moisil in 1960. Such (incomplete) rules can be formulated as context-sensitive grammars, since the alternations are determined by the context in which certain characters appear.
This leads to the idea of analyzing the verbs from a machine learning point of view: what can we find out by looking at n-gram representation of the infinitives?
This is easy to do within scikits.learn. The feature_extraction.text
package contains all the necessary tools: the CharNGramExtractor,
which builds all the n-grams of a string, for n in an interval. Then, a
CountVectorizer is built on top of the extractor. Its purpose is to
extract the features out of a list of documents and transform them into
a matrix representation of token counts. By postprocessing this matrix
we can obtain a binary representation, indicating only whether a token
occurs in a document or not, instead of the count.
In this case, documents are Romanian infinitives. This means we are limited to using short n-grams, because the documents are themselves short. There is also the question whether anything relevant can be found out of such a representation which does not encode a lot of information.
After building the data matrix from the list of verbs, I plotted a 2D PCA projection and here are the results. I am only posting a teaser for now, but the results are encouraging:
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From the image it is clear that n-gram representations of the infinitives induce clusters. Further results suggest that for certain subclasses of the dataset, such a representation (or even a simpler one) is enough to clearly answer whether a verb does not exhibit alternations. This encourages further exploration of this path, especially supervised and semi-supervised approaches.
[]: http://localhost:8001/wp-content/uploads/2011/04/infinitives_pca.png
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