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The LR syntax analysis method is a useful and versatile technique for parsing deterministic context-free languages in compiling applications. This paper provides an efficient Fuzzy Canonical LR(1)(FCLR) parser for Fuzzy context-free grammars. The FCLR (1) parser splits states based on differing lookahead sets, it can have many more states than the corresponding Simple LR (1) (SLR) or LR (0) parser. Potentially it could require splitting a state with just one item into a different state for each subset of the possible lookaheads; in a pathological case, this means the entire power set of its follow set. It never actually gets that bad in practice, but a FCLR (1) parser for a programming language might have an order of magnitude more states than an SLR (1) parser. KeywordsGrammars, Parsers, Compilers, Context-free languages, LR grammars. Natural Language Parsing Using Fuzzy Canonical Lr (1) Parser 349 context-free grammar [4], has been introduced to express uncertainty, ambiguity, and vagueness in natural language fragments [1]. Here production rules are written by considering noun phrase(NP), verb phrase(VP), preposition(PP), adjective (ADJP) [2] and fuzzy membership values are assigned for each rule. Membership values are assigned randomly to each of these rules, finally the values for the entire set of rules for each phrase sums up to 1. Consider commonly used Production rules for construction of English language sentences are as follows 1) SNP VP (1.0) 11) VPv NP (0.1) 2) S aux NP VP (1.0) 12) VP v VP (0.1) 3) NPart n (0.2) 13) VP v NP VP (0.2) 4) NPpron (0.2) 14) VP v ADJP (0.1) 5) NP n (0.1) 15) VP TO VP (0.2) 6) NP NP PP (0.2) 16) VP v NP PP (0.1) 7) NP propn (0.1) 17) VP v PP (0.1) 8) NP NOM (0.2) 18) PPprep NP (1.0) 9) NOMadj n (1.0) 19) ADJPadj (0.5) 10) VPv (0.1) 20) ADJPadj VP (0.5) 21) TO to (1.0) A. Parsing Parser for a grammar to be a device which, when presented with an input string, attempts to construct a derivation tree whose frontier matches the input. If the parser can construct such a derivation tree, then it will have verified that the input string is a sentence of the language generated by the grammar. If the input is syntactically incorrect, then the tree construction process will not succeed and the positions at which the process falters can be used to indicate possible error locations. A parser can operate in many different ways. In this paper we shall restrict ourselves to parsers that examine the input string from left to right, one symbol at a time. These parsers will attempt to construct the derivation tree "bottom-up"; i.e., from the leaves to the root. For historical reasons, these parsers are called LR parsers. The "L" stands for "left-to-right scan of the input", the "R" stands for "rightmost derivation." We shall see that an LR parser operates by reconstructing the reverse of a rightmost derivation for the input. In this section we shall describe in an informal way how a certain class of LR parsers, called LR(1) parseoperate. An LR parser deals with a sequence of partially built trees during its tree construction process. We shall loosely call this sequence of trees a forest. In our framework the forest is built from left to right as the input is read. At a particular stage in the construction process, we have read a certain amount of the input, and we have a partially constructed derivation tree [3]. For example, suppose that we are parsing the input string 'a,b' according to the grammar (ie. After reading the first 'a' we construct the tree. 1) Algorithm for Item Sets: Set Of Items clousure ( I ) { J = I; repeat for (each item A α .B β in J) where λ i = [0..1] for (each production B γ of G) if (B . γ is not in J) add B . γ to J ; Until no more items are added to J on one round : Return J ; }  λi

NATURAL LANGUAGE PARSING USING FUZZY CANONICAL LR (1) PARSER