Dynamic Fuzzy Parser to Parse English Sentence Using POS Tagger and Fuzzy Max-Min Technique

Natural Language (NL) is an essential part of ourlife. Humans use language for communication. NL is a prevailing tool used by the humans to convey the information. Natural Language Understanding (NLU) is a major challenge in Natural Language Processing (NLP). NLP is a part of Artificial Intelligence (AI). NLP provides a significant tool for communication. It attempts to produces noise free data and conversion of noise to text. NLU is having different levels. This paper presents the issue with respect to one of the level such as syntax analysis. To provide a solution for syntax analysis, dynamic fuzzy parser is designed and implemented to parse the English input sentences. Traditional approach of parsing is enhanced by applying fuzzy logic. This helps to know the syntactic correctness of the sentence. Penns tree bank parts of speech tags are used for the Parts of Speech Tagger (POS). POS tagger assigns the parts of speech tags for the input English sentence. Then these tags of the words are parsed using the grammar rules. Finally the result is displayed to represent the number of words parsed in a sentence with its associated fuzzy membership value. This parser produces Precision value of 1(100%), Recall value of 0.92 (92%) and F-measure value of 0.9583 for the sample of 50 correct and 50 incorrect sentences.