Eckhard Bick - VISL

While undeniably involving more context than HMMs, probabilistic CFGs suffer
from the same lexicalisation problem and to a much higher degree from scarceness of
hand-tagged training material (while the higher complexity involved would demand
more training data, there is actually less material available 90 ). One of the core problems
of PCFGs is deeply rooted in the assumption of "context-free-ness" itself: the
probability of a given production is wrongly supposed to be the same everywhere. Still,
linguistic context like the function and dependency of the non-terminal in question, will
obviously have a strong influence on this probability. NPs, for instance, are more likely
to be definite (i.e. expand into 'det-def N' or pronouns) in subject position than in direct
object position. While function and dependency are easily available context conditions
in Constraint Grammar, they would have to be expressed in a more implicit way in
PCFGs. An NP's subject function, for example, might in English be expressed by stating
that the NP in question is the first NP in a 'S -> NP VP' production happening to be
describing the NP's mother node, and the conditional probability concerned would then
read: p(NP -> det-def N | NP in S -> NP VP).
Current Constraint Grammars, on the other hand, have only crude tools at their
disposal for exploiting statistical tendencies in collocational patterns, like lexically
marking certain readings as , or ordering rules in successively applied sets of
less and less safe, or more and more heuristic character. Such rule hierarchies mimic, in
a way, the rule probabilities of PCFGs, yet without the latter's mathematical precision.
State-of-the-art probabilistic PoS-taggers can now compete with traditional rule
based systems and achieve correctness rates of 96-97%. Probabilistic taggers also
provide a good base line against which to measure any other tagger: even zero-order
HMM, i.e. where each word simply is assigned its post likely PoS, have a correctness
rate of 91-92%, for English (Eeg-Olofsson, 1991).
Early systems computed both lexical probabilities and Markov Model PoS
transition probabilities from tagged corpora, as - for English - in (Church, 1988) and in
the LOB-tagging system, CLAWS (Garside, 1987), where a success rate of 96-97% is
reported for a mixed tag sets of PoS, inflexion and - for a few words - base form. By
using techniques like the Baum-Welch algorithm, lexica with different tag sets can be
used as a starting point, with only ordinary text to train on. In (Cutting et. al., 1992), for
example, 96% correctness is claimed for recovering PoS tags from the tagged Brown
Corpus (Francis and Kucera, 1992), using only a lexicon and untagged training text
from the same corpus. With yet another probabilistic approach, Ratnaparkhi's
maximum-entropy tagger (Ratnaparkhi, 1996) claims 97% accuracy on WSJ text when
trained on the Penn Treebank (Marcus et al., 1993). In (Brill, 1992) automatically
learned trigram transformation rules are used in combination with a simple zero-order
stochastic tagger, with error rates around 5% when using a tagged training corpus but
90 For English, the 100.000 word syntactically annotated Suzanne corpus does provide such training data, but it must still be
considered a corpus of rather modest size when compared to the market of purely PoS-tagged corpora.
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