Learning to Disambiguate Syntactic Relations
نویسنده
چکیده
Natural Language is highly ambiguous, on every level. This article describes a fast broadcoverage state-of-the-art parser that uses a carefully hand-written grammar and probabilitybased 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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