AG 2   Info

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Saarbrücken

We present a system for parsing and translating natural language that learns from examples. As our parsing model we choose a deterministic shift-reduce type parser that integrates part-of-speech tagging and syntactic and semantic processing. Applying machine learning techniques, the system uses parse action examples acquired under supervision or from a treebank to generate a parser in the form of a decision structure, a generalization of decision trees.

To learn good parsing and translation decisions, our system relies heavily
on context as encoded in currently up to 205 features describing the morpho-
logical, syntactical and semantical aspects of a given parse state. The
context
features easily integrate various types of background knowledge. Compared with
recent probabilistic systems that were trained on 40,000 sentences, our system
relies on more background knowledge and a deeper analysis, but fewer examples,
currently 256 sentences for English and 2048 for Japanese.

We test our parser on lexically limited sentences from the Wall Street Journal
and achieve accuracy rates of 89.8% for labeled precision, 98.4% for part of
speech tagging and 56.3% of test sentences without any crossing brackets.
Machine translations of 32 Wall Street Journal sentences to German have been
evaluated by 10 bilingual volunteers and been graded as 2.4 on a 1.0 (best) to
6.0 (worst) scale for both grammatical correctness and meaning preservation.
We finally present recent results of parsing lexically unlimited sentences
from the Japanese newspaper Mainichi.

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