Sentence Parsing for Afan Oromo using Supervised Learning Techniques - Degefa, Diriba Megersa
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NLP should not be considered as an isolated system and characterizes its performance mainly with respect to a gold standard result. Rather its performance is determined by its use in a more complex setting, either as an embedded system or serving a precise function for a human user. The area of NLP is broad and wide in scope. Hence, this book highlights syntactic disambiguation and parsing using statistical methods. It includes morphological analyzer and part of speech tagger drawn from computational linguistics and developed using machine learning techniques. I hope it will contribute…mehr

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Produktbeschreibung
NLP should not be considered as an isolated system and characterizes its performance mainly with respect to a gold standard result. Rather its performance is determined by its use in a more complex setting, either as an embedded system or serving a precise function for a human user. The area of NLP is broad and wide in scope. Hence, this book highlights syntactic disambiguation and parsing using statistical methods. It includes morphological analyzer and part of speech tagger drawn from computational linguistics and developed using machine learning techniques. I hope it will contribute something towards the efforts of developing a full-fledged NLP system that can be used in languages such as Oromo (most spoken language in Africa) on which the book is based.
Autorenporträt
Diriba M. Degefa is specialized in Natural Language Processing (NLP) using probabilistic and statistical methods.He promotes the use of Statistical approaches in automated language processing, including probabilistic modeling, information theory, and linear algebra using the technology from machine learning.