404 research outputs found
Efficient Normal-Form Parsing for Combinatory Categorial Grammar
Under categorial grammars that have powerful rules like composition, a simple
n-word sentence can have exponentially many parses. Generating all parses is
inefficient and obscures whatever true semantic ambiguities are in the input.
This paper addresses the problem for a fairly general form of Combinatory
Categorial Grammar, by means of an efficient, correct, and easy to implement
normal-form parsing technique. The parser is proved to find exactly one parse
in each semantic equivalence class of allowable parses; that is, spurious
ambiguity (as carefully defined) is shown to be both safely and completely
eliminated.Comment: 8 pages, LaTeX packaged with three .sty files, also uses cgloss4e.st
Principles and Implementation of Deductive Parsing
We present a system for generating parsers based directly on the metaphor of
parsing as deduction. Parsing algorithms can be represented directly as
deduction systems, and a single deduction engine can interpret such deduction
systems so as to implement the corresponding parser. The method generalizes
easily to parsers for augmented phrase structure formalisms, such as
definite-clause grammars and other logic grammar formalisms, and has been used
for rapid prototyping of parsing algorithms for a variety of formalisms
including variants of tree-adjoining grammars, categorial grammars, and
lexicalized context-free grammars.Comment: 69 pages, includes full Prolog cod
A syntactic language model based on incremental CCG parsing
Syntactically-enriched language models (parsers) constitute a promising component in applications such as machine translation and speech-recognition. To maintain a useful level of accuracy, existing parsers are non-incremental and must span a combinatorially growing space of possible structures as every input word is processed. This prohibits their incorporation into standard linear-time decoders. In this paper, we present an incremental, linear-time dependency parser based on Combinatory Categorial Grammar (CCG) and classification techniques. We devise a deterministic transform of CCGbank canonical derivations into incremental ones, and train our parser on this data. We discover that a cascaded, incremental version provides an appealing balance between efficiency and accuracy
Supertagged phrase-based statistical machine translation
Until quite recently, extending Phrase-based Statistical Machine Translation (PBSMT) with syntactic structure caused system performance to deteriorate. In this work we show that incorporating lexical syntactic descriptions in the form of supertags can yield significantly better PBSMT systems. We describe a novel PBSMT model that integrates
supertags into the target language model and the target side of the translation model. Two kinds of supertags are employed: those from Lexicalized Tree-Adjoining Grammar
and Combinatory Categorial Grammar. Despite the differences between these two approaches, the supertaggers give similar improvements. In addition to supertagging, we also explore the utility of a surface global grammaticality measure based on combinatory operators. We perform various experiments on the Arabic to English NIST 2005 test set addressing issues such as sparseness, scalability and the utility of system subcomponents. Our best result (0.4688 BLEU) improves by 6.1% relative to a state-of-theart
PBSMT model, which compares very favourably with the leading systems on the NIST 2005 task
Efficient Parsing for French
International audienceParsing with categorial grammars often leads to problems such as proliferating lexical ambiguity, spurious parses and overgeneration. This paper presents a parser for French developed on an unification based categorial grammar (FG) which avoids these problems. This parser is a bottom-up chart parser augmented with a heuristic eliminating spurious parses. The unicity and completeness of parsing are proved
\u3ci\u3eCorrect Reasoning: Essays on Logic-Based AI in Honour of Vladimir Lifschitz\u3c/i\u3e
Co-edited by Yuliya Lierler, UNO faculty member.
Essay, Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming, co-authored by Yuliya Lierler, UNO faculty member.
This Festschrift published in honor of Vladimir Lifschitz on the occasion of his 65th birthday presents 39 articles by colleagues from all over the world with whom Vladimir Lifschitz had cooperation in various respects. The 39 contributions reflect the breadth and the depth of the work of Vladimir Lifschitz in logic programming, circumscription, default logic, action theory, causal reasoning and answer set programming.https://digitalcommons.unomaha.edu/facultybooks/1231/thumbnail.jp
A Structural Interpretation of Combinatory Categorial Grammar
This paper gives an interpretation of Combinatory Categorial Grammar derivations in terms of the construction of traditional phrase structure trees. This structural level of representation not only shows how CCG is related to other grammatical investigations, but this paper also uses it to extend CCG in ways which are useful for analyzing and parsing natural language, including a better analysis of coordination
Parsing Combinatory Categorial Grammar with Answer Set Programming: Preliminary Report
Combinatory categorial grammar (CCG) is a grammar formalism used for natural
language parsing. CCG assigns structured lexical categories to words and uses a
small set of combinatory rules to combine these categories to parse a sentence.
In this work we propose and implement a new approach to CCG parsing that relies
on a prominent knowledge representation formalism, answer set programming (ASP)
- a declarative programming paradigm. We formulate the task of CCG parsing as a
planning problem and use an ASP computational tool to compute solutions that
correspond to valid parses. Compared to other approaches, there is no need to
implement a specific parsing algorithm using such a declarative method. Our
approach aims at producing all semantically distinct parse trees for a given
sentence. From this goal, normalization and efficiency issues arise, and we
deal with them by combining and extending existing strategies. We have
implemented a CCG parsing tool kit - AspCcgTk - that uses ASP as its main
computational means. The C&C supertagger can be used as a preprocessor within
AspCcgTk, which allows us to achieve wide-coverage natural language parsing.Comment: 12 pages, 2 figures, Proceedings of the 25th Workshop on Logic
Programming (WLP 2011
- âŠ