NLP Markov: Adding Genitive, Dative And Ablative Cases

The genitive, dative and ablative are grammar cases that modify nouns and verbs. These cases form a bulk of the written and coded literature published in the internet. Buried in these paragraphs are grammatical cases supporting an idea, an intangible concept, an emotional artifact.

Classifying these embedded grammatical constructs require a good number of tools still waiting to be developed. There are probably tools that are unknown at the time of this writing and they will be found sometime later.

One approach is by book catalog method which hasn’t been designed and implemented, yet. This approach involve deconstructing a document into several basic forms, for example outline and timeline format, then placing them in a catalog. Each node in the timeline is a taggable item where elements can attach.

Markov Chain algorithm have proven to be very useful in this regard as I was able to setup a probability function for a given lexeme. Granularity is currently at the word level, and it would be interesting if the algorithm perform well at the phrase level where these three grammar cases become significant.

One example of Markov Chain implementation is the Google Toolbar. I noticed not too long ago a new behavior was added to the search dropdown box. I type-in a word and below appears a list of related words. These related words–I would imagine–are words generated by Markov Chain. What I found interesting about the list was the X number of words per row. I can’t tell what that does imply, but it does mean something big and grand.

There is still a disconnect between the output of a Markov algorithm with respect to “Phrase-ology” in the sense that a Markov algorithm is dependent on another variable which is not identified at the moment.

Solving the unknown would mean extending the parameters to N to cover for a subset of the entire phrase. Tagging may become optional in this case, though.

This is definitely an exciting experiment worth looking into. The question of simply applying Markov algorithm as a one-all solution brush; or treat it as a tool-in-a-toolbox, combining it with other methods to achieve a finer richer solution.

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  1. Advanced Natural Language Processing
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