Markov substitute processes : a new model for linguistics and beyond

We introduce Markov substitute processes, a new model at the crossroad of statistics and formal grammars, and prove its main property : Markov substitute processes with a given support form an exponential family.

Toric grammars: a new statistical approach to natural language modeling

We propose a new statistical model for computational linguistics. Rather than trying to estimate directly the probability distribution of a random sentence of the language, we define a Markov chain on finite sets of sentences with many finite recurrent communicating classes and define our language model as the invariant probability measures of the chain on each recurrent communicating class. This Markov chain, that we call a communication model, recombines at each step randomly the set of sentences forming its current state, using some grammar rules. When the grammar rules are fixed and known in advance instead of being estimated on the fly, we can prove supplementary mathematical properties. In particular, we can prove in this case that all states are recurrent states, so that the chain defines a partition of its state space into finite recurrent communicating classes. We show that our approach is a decisive departure from Markov models at the sentence level and discuss its relationships with Context Free Grammars. Although the toric grammars we use are closely related to Context Free Grammars, the way we generate the language from the grammar is qualitatively different. Our communication model has two purposes. On the one hand, it is used to define indirectly the probability distribution of a random sentence of the language. On the other hand it can serve as a (crude) model of language transmission from one speaker to another speaker through the communication of a (large) set of sentences.

A probabilistic top-down parser for minimalist grammars

This paper describes a probabilistic top-down parser for minimalist grammars. Top-down parsers have the great advantage of having a certain predictive power during the parsing, which takes place in a left-to-right reading of the sentence. Such parsers have already been well-implemented and studied in the case of Context-Free Grammars, which are already top-down, but these are difficult to adapt to Minimalist Grammars, which generate sentences bottom-up. I propose here a way of rewriting Minimalist Grammars as Linear Context-Free Rewriting Systems, allowing to easily create a top-down parser. This rewriting allows also to put a probabilistic field on these grammars, which can be used to accelerate the parser. Finally, I propose a method of refining the probabilistic field by using algorithms used in data compression.