85 research outputs found
Robust Processing of Natural Language
Previous approaches to robustness in natural language processing usually
treat deviant input by relaxing grammatical constraints whenever a successful
analysis cannot be provided by ``normal'' means. This schema implies, that
error detection always comes prior to error handling, a behaviour which hardly
can compete with its human model, where many erroneous situations are treated
without even noticing them.
The paper analyses the necessary preconditions for achieving a higher degree
of robustness in natural language processing and suggests a quite different
approach based on a procedure for structural disambiguation. It not only offers
the possibility to cope with robustness issues in a more natural way but
eventually might be suited to accommodate quite different aspects of robust
behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro,
pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th
German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture
Notes in Computer Science, Springer 199
Order and structure
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 1996.Includes bibliographical references (p. [291]-306).by Colin Phillips.Ph.D
Structure Sharing and Parallelization in a GB Parser
By utilizing structure sharing among its parse trees, a GB parser can increase its efficiency dramatically. Using a GB parser which has as its phrase structure recovery component an implementation of Tomita's algorithm (as described in [Tom86]), we investigate how a GB parser can preserve the structure sharing output by Tomita's algorithm. In this report, we discuss the implications of using Tomita's algorithm in GB parsing, and we give some details of the structuresharing parser currently under construction. We also discuss a method of parallelizing a GB parser, and relate it to the existing literature on parallel GB parsing. Our approach to preserving sharing within a
shared-packed forest is applicable not only to GB parsing, but anytime we want to preserve structure sharing in a parse forest in the presence of features
Recommended from our members
A Schrift to Fest Kyle Johnson
This volume of forty-three papers celebrates Kyle Johnson\u27s contribution to linguistics. Written by Johnsonâs colleagues and former students, the papers touch upon topics that have defined Johnsonâs career, including verb movement, ellipsis, gapping, Germanic, extraposition, quantifiers and determiners, object positions, among others.https://scholarworks.umass.edu/linguist_oapubs/1000/thumbnail.jp
Extensible Dependency Grammar: a modular grammar formalism based on multigraph description
This thesis develops Extensible Dependency Grammar (XDG), a new grammar formalism combining dependency grammar, model-theoretic syntax, and Jackendoff\u27;s parallel grammar architecture. The design of XDG is strongly geared towards modularity: grammars can be modularly extended by any linguistic aspect such as grammatical functions, word order, predicate-argument structure, scope, information structure and prosody, where each aspect is modeled largely independently on a separate dimension. The intersective demands of the dimensions make many complex linguistic phenomena such as extraction in syntax, scope ambiguities in the semantics, and control and raising in the syntax-semantics interface simply fall out as by-products without further stipulation. This thesis makes three main contributions: 1. The first formalization of XDG as a multigraph description language in higher order logic, and investigations of its expressivity and computational complexity. 2. The first implementation of XDG, the XDG Development Kit (XDK), an extensive grammar development environment built around a constraint parser for XDG. 3. The first application of XDG to natural language, modularly modeling a fragment of English
A Competitve Attachment Model for Resolving Syntactic Ambiguities in Natural Language Parsing
Linguistic ambiguity is the greatest obstacle to achieving practical
computational systems for natural language understanding. By
contrast, people experience surprisingly little difficulty in
interpreting ambiguous linguistic input. This dissertation explores
distributed computational techniques for mimicking the human ability
to resolve syntactic ambiguities efficiently and effectively. The
competitive attachment theory of parsing formulates the processing of
an ambiguity as a competition for activation within a hybrid
connectionist network. Determining the grammaticality of an input
relies on a new approach to distributed communication that integrates
numeric and symbolic constraints on passing features through the
parsing network. The method establishes syntactic relations both
incrementally and efficiently, and underlies the ability of the model
to establish long-distance syntactic relations using only local
communication within a network. The competitive distribution of
numeric evidence focuses the activation of the network onto a
particular structural interpretation of the input, resolving
ambiguities. In contrast to previous approaches to ambiguity
resolution, the model makes no use of explicit preference heuristics
or revision strategies. Crucially, the structural decisions of the
model conform with human preferences, without those preferences having
been incorporated explicitly into the parser. Furthermore, the
competitive dynamics of the parsing network account for additional
on-line processing data that other models of syntactic preferences
have left unaddressed.
(Also cross-referenced as UMIACS-TR-95-55
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