LR Parsing for LCFRS

23282

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nan

Abstract

LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG). It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Gram- mar. In this paper, we present the first LR- style parsing algorithm for Linear Context- Free Rewriting Systems (LCFRS), a mildly context-sensitive extension of CFG which has received considerable attention in the last years. 1

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Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL, pages 1250–1255, Denver, Colorado, May 31 – June 5, 2015. c⃝2015 Association for Computational Linguistics LR Parsing for LCFRS Laura Kallmeyer and Wolfgang Maier Institute for Language and Information University of D¨usseldorf D¨usseldorf, Germany {kallmeyer,maierwo}@phil.hhu.de Abstract LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG). It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Gram- mar. In this paper, we present the first LR- style parsing algorithm for Linear Context- Free Rewriting Systems (LCFRS), a mildly context-sensitive extension of CFG which has received considerable attention in the last years. 1 Introduction LR parsing is an incremental shift-reduce parsing strategy in which the transitions between parser states are guided by an automaton which is com- piled offline. LR parsers were first introduced for deterministic context-free languages (Knuth, 1965) and later generalized to context-free lan- guages (Tomita, 1984) and tree-adjoining languages (Nederhof, 1998; Prolo, 2003). Linear Context-Free Rewriting System (LCFRS) (Vijay-Shanker et al., 1987) is an immediate ex- tension of CFG in which each non-terminal can cover more than one continuous span of the in- put string. LCFRS and equivalent formalisms have been used for the modeling of discontinu- ous constituents (Maier and Lichte, 2011) and non- projective dependencies (Kuhlmann, 2013), as well as for data-driven parsing of such structures (Maier and Kallmeyer, 2010; Kallmeyer and Maier, 2013; van Cranenburgh, 2012; Angelov and Ljungl¨of, 2014). They have also been used for modeling non-concatenative morphology (Botha and Blun- som, 2013), for grammar engineering (Ranta, 2011), and for modeling alignments in machine translation (Søgaard, 2008; Kaeshammer, 2013). To our knowl- edge, so far, no LR strategy for LCFRS has been presented in the literature. In this paper, we present an LR-style parser for LCFRS. It is based on the in- cremental parsing strategy implemented by Thread Automata (Villemonte de la Clergerie, 2002). The remainder of the article is structured as fol- lows. In the following section, we introduce LCFRS and thread automata. Section 3 presents the algo- rithm along an example. In particular, section 3.2 gives the algorithms for automaton and parse table constructions, and section 3.3 presents the parsing algorithm. Section 4 concludes the article. 2 Preliminaries 2.1 LCFRS In this paper, we restrict ourselves to string rewriting LCFRS and omit the more general definition (Weir, 1988). In LCFRS, a single non-terminal can span k ≥ 1 continuous blocks of a string. A CFG is simply a special case of an LCFRS in which k = 1. k is called the fan-out of the non-terminal. We notate LCFRS with the syntax of Simple Range Concate- nation Grammars (SRCG) (Boullier, 1998), a for- malism equivalent to LCFRS. An LCFRS1 (Vijay-Shanker et al., 1987; Seki et al., 1991) is a tuple G = (N, T, V, P, S) where N 1Note that for purposes of exposition, we limit ourselves to ε-free LCFRS. 1250 is a finite set of non-terminals with a function dim: …

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Labeled Text

LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG). It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Grammar. In this paper, we present the first LRstyle parsing algorithm for Linear ContextFree Rewriting Systems (LCFRS), a mildly context-sensitive extension of CFG which has received considerable attention in the last years. 1

Publication Details

Paper ID:
23282
Year:
2015
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan