Open AccessLearning to Disambiguate Syntactic Relations
Author(s) -
Gerold Schneider
Publication year - 2003
Publication title -
linguistik online
Language(s) - English
Resource type - Journals
ISSN - 1615-3014
DOI - 10.13092/lo.17.789
Subject(s) - computer science , artificial intelligence , natural language processing , parsing , grammar , process (computing) , natural language , grammar induction , linguistics , rule based machine translation , programming language , philosophy
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.