Notes on computational linguistics.pdf - UCLA Department of ...

Stabler - Lx 185/209 2003
(100) Abney (1996a, p21) says:
In fact, probabilities make Markov models more adequate then their non-probabilistic counterparts,
not less adequate. Markov models are surprisingly effective, given their finite-state
substrate. For example, they are the workhorse of speech recognition technology. Stochastic
grammars can also be easier to learn than their non-stochastic counterparts…
We might agree about the interest of (non-finite state) stochastic grammars. Certainly, developing
stochastic grammars, one of the main questions is: which grammars, which structural relations do we
find in human languages? This is the traditional focus of theoretical linguistics. As for the stochastic
influences, it is not yet clear what they are, or how revealing they will be.
As for the first sentence in this quoted passage, and the general idea that we can develop good stochastic
models without attention to the expressive capabilities of the “substrate,” you decide.
(101) It is quite possible that “lexical activation” is sensitive to word co-occurrence frequencies, and this might
be modeled with a probabilistic finite automaton (e.g. a state-labeled Markov model or a standard,
transition-labeled probabilistic fsa).
The problem of detecting stochastic influences in the grammar itself depends on knowing what parts of
the grammar depend on the lexical item. In CFGs, for example, we get only a simple category for each
word, but in lexicalized TAGs, and in recent transformational grammars, the lexical item can provide a
rich specification of its role in derivations.
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