Interleaving Syntax and Semantics in an Effecient Bottom-Up Parser (original) (raw)
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Interleaving syntax and semantics in an efficient bottom-up parser
Proceedings of the 32nd annual meeting on Association for Computational Linguistics -, 1994
We describe an efficient bottom-up parser that interleaves syntactic and semantic structure building. Two techniques are presented for reducing search by reducing local ambiguity: Limited leftcontext constraints are used to reduce local syntactic ambiguity, and deferred sortal-constraint application is used to reduce local semantic ambiguity. We experimentally evaluate these techniques, and show dramatic reductions in both number of chart edges and total parsing time. The robust processing capabilities of the parser are demonstrated in its use in improving the accuracy of a speech recognizer.
OPTIMAL AMBIGUITY PACKING IN CONTEXT-FREE PARSERS WITH INTERLEAVED UNIFICATION
Ambiguity packing is a well known technique for enhancing the efficiency of context-free parsers. However, in the case of unification-augmented context-free parsers where parsing is interleaved with feature unification, the propagation of feature structures imposes difficulties on the ability of the parser to effectively perform ambiguity packing. We demonstrate that a clever heuristic for prioritizing the execution order of grammar rules and parsing actions can achieve a high level of ambiguity packing that is provably optimal. We present empirical evaluations of the proposed technique, performed with both a Generalized LR parser and a chart parser, that demonstrate its effectiveness.
Ambiguity packing in constraint-based parsing: practical results
2000
We describe a novel approach to 'packing' of local ambiguity in parsing with a wide-coverage HPSG grammar, and provide an empirical assessment of the interaction between various packing and parsing strategies. We present a linear-time, bidirectional subsumption test for typed feature structures and demonstrate that (a) subsumption-and equivalence-based packing is applicable to large HPSG grammars and (b) average parse complexity can be greatly reduced in bottom-up chart parsing with comprehensive HPSG implementations.
Robustness in language and speech technology, 2001
Large practical NLP applications require robust analysis components that can effectively handle input that is disfluent or extra-grammatical. The effectiveness and efficiency of any robust parser are a direct function of three main factors: (1) Flexibility: what types of disfluencies and deviations from the grammar can the parser handle?; (2) Search: How does the parser search the space of possible interpretations, and what techniques are applied to prune the search space?; and (3) Parse Selection and Disambiguation: What methods and resources are used to evaluate and rank potential parses and sub-parses, and how does the parser cope with the extreme levels of ambiguity introduced by its flexibility parameters? In this chapter we describe our investigations on how to balance flexibility and efficiency in the context of two different robust parsers -a GLR parser and a left corner Chart parser -both based on a unification-augmented context-free grammar formalism. We demonstrate how the combination of a beam search together with ambiguity packing and statistical disambiguation provide a flexible framework for achieving a good balance between robustness and efficiency in such parsers. Our investigations are based on experimental results and comparative performance evaluations of both parsers using a grammar for the spoken language ESST (English Spontaneous Scheduling Task) domain.
A Parser for Portable NL Interfaces Using Graph-Unification-Based Grammars
This paper presents the reasoning behind the selection and design of a parser for the Lingo project on natural language interfaces at MCC. The major factors in the selection of the parsing algorithm were the choices of having a syntactically based grammar, using a graph-unification-based representation language, using Combinatory Categorial Grammars, and adopting a one-to-many mapping from syntactic bracketings to semantic representations in certain cases. The algorithm chosen is a variant of chart parsing that uses a best-first control structure managed on an agenda. It offers flexibility for these natural language processing applications by allowing for best-first tuning of parsing for particular grammars in particular domains while at the same time allowing exhaustive enumeration of the search space during grammar development. Efficiency advantages of this choice for graph- unification-based representation languages are outlined, as well as a number of other advantages that acrue...
A Bottom-Up Algorithm for Parsing and Generation
We present a bottom-up algorithm for parsing and generation. The algorithm is a bottom-up chart parser, whose lexical lookup phase has been modified for generation. An analysis of the algorithm offers interesting insights into the relationship between parsing and generation, summarized by the statement that parsing is a very constrained form of generation. The use of the generation algorithm as a component of a grammar development environment is discussed.
Deeper syntax for better semantic parsing
2016
Syntax plays an important role in the task of predicting the semantic structure of a sentence. But syntactic phenomena such as alternations, control and raising tend to obfuscate the relation between syntax and semantics. In this paper we predict the semantic structure of a sentence using a deeper syntax than what is usually done. This deep syntactic representation abstracts away from purely syntactic phenomena and proposes a structural organization of the sentence that is closer to the semantic representation. Experiments conducted on a French corpus annotated with semantic frames showed that a semantic parser reaches better performances with such a deep syntactic input.