Learning to Disambiguate Syntactic Relations

Gerold Schneider

Abstract


Natural Language is highly ambiguous, on every level. This article describes a fast broad-coverage state-of-the-art parser that uses a carefully hand-written grammar and probability-based machine learning approaches on the syntactic level. It is shown in detail which statistical learning models based on Maximum-Likelihood Estimation (MLE) can support a highly developed linguistic grammar in the disambiguation process.

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DOI: http://dx.doi.org/10.13092/lo.17.789