Eckhard Bick - VISL

3.5 Tools for disambiguation
In corpus linguistics, most systems of automatic analysis can be classified by measuring
them against the bipolarity of rule based versus probabilistic approaches. Thus,
Karlsson (1995) distinguishes between “pure” rule based or probabilistic systems,
hybrid systems and compound systems, i.e. rule based systems supplemented with
probabilistic modules, or probabilistic systems with rule based “bias” or postprocessing.
As a second parameter, lexicon dependency might be added, since both rules based and
probabilistic systems differ internally as to how much use they make of extensive
lexica, both in terms of lexical coverage and granularity of lexical information.
Typically, in terms of computational viability, probabilistic systems are good at
lower level analysis, especially word class (part of speech, PoS) annotation and speech
recognition, while rule based systems have been preferred for higher level annotation,
like constituent trees and argument structure. As a result of this polarisation, the older -
linguistically motivated - term "parsing", though derived from "pars orationis" (part of
speech) has come to mean, more narrowly, higher level syntactic analysis, while the
newer - computationally motivated - term "tagging" has mostly been limited to lower
level PoS-annotation, - which is the obvious application for at leastword based tags.
Even implementationally, the bipolarity is quite distinct: The archetypal rule based
systems, PSG grammars and their descendants, have embraced declarative programming
languages like Prolog and Lisp, while probabilistic systems huddle together around the
Hidden Markov Model using procedural programming languages like C or - for
statistics proper - common UNIX-tools like sort, uniq, awk and perl.
With the advent of larger, multi-million word corpora, apart from annotation
speed, error rates have become more crucial, since manual post-processing is becoming
less and less feasible. On the one hand, this should favour rule-based systems, since
they can - at least in theory - be made more "perfect", so the high initial price in man
power for writing a grammar should pay off for large corpora - the larger the corpus the
better the investment. On the other hand, large corpora supply better training facilities
for the "cheap" probabilistic systems and should thus make them more accurate 88 . Yet
again, since what is really needed, are tagged training corpora, co-operation between
systems might be the best solution. This, however, presupposes more or less compatible
category definitions and tag sets, which is, in spite of normalising initiatives like the
EU's EAGLES convention (Monachini and Calzolari, 1996) far from being a reality
today.
88 For a tagset of 50 PoS-inflexion tags or tag chains, for example, it is as hard to train trigrams on a million word corpus as
it is to train tetragrams on a 50 million word corpus, the reason being, that the number is only 8 times as high as the number
of different n-grams. Training trigrams on a hundred million word corpus, however, yields on average 800 examples of each
trigram combination - even when ignoring the relatively higher frequency of the more relevant trigrams -, which should be
enough to do statistics on.
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