Probabilistic context free grammars and their challenges in computational learning theory Kumar K. Senthil1,*, Malathi D.2 1Asst. Prof Department of CSE, SRMIST, Kattankulathur 2Professor, Department of CSE, SRMIST, Kattankulathur *Corresponding Author: K. Senthil Kumar Asst. Prof, Department of CSE, SRMIST, Katankulathur-603203 Email: senthilkumar.k@ktr.srmuniv.ac.in
Online published on 16 October, 2018. Abstract Probabilistic context free grammars have applications in natural language processing, Bio-informatics, pattern recognition and Machine learning. In natural language processing one always finds uncertainty and ambiguity in parsing sentences. When the size of a sentence increases it always creates lot of ambiguity and also very difficult to represent those by available grammar. The motivation to use probabilistic context free grammars is their ability to handle those situations. In this paper we mainly analyze Probabilistic context free grammar and its challenges in computational linguistic. Top Keywords Probabilistic context free grammar, CYK Parsing, Tree bank, supervised learning, unsupervised learning. Top |