945 research outputs found
Multiple Context-Free Tree Grammars: Lexicalization and Characterization
Multiple (simple) context-free tree grammars are investigated, where "simple"
means "linear and nondeleting". Every multiple context-free tree grammar that
is finitely ambiguous can be lexicalized; i.e., it can be transformed into an
equivalent one (generating the same tree language) in which each rule of the
grammar contains a lexical symbol. Due to this transformation, the rank of the
nonterminals increases at most by 1, and the multiplicity (or fan-out) of the
grammar increases at most by the maximal rank of the lexical symbols; in
particular, the multiplicity does not increase when all lexical symbols have
rank 0. Multiple context-free tree grammars have the same tree generating power
as multi-component tree adjoining grammars (provided the latter can use a
root-marker). Moreover, every multi-component tree adjoining grammar that is
finitely ambiguous can be lexicalized. Multiple context-free tree grammars have
the same string generating power as multiple context-free (string) grammars and
polynomial time parsing algorithms. A tree language can be generated by a
multiple context-free tree grammar if and only if it is the image of a regular
tree language under a deterministic finite-copying macro tree transducer.
Multiple context-free tree grammars can be used as a synchronous translation
device.Comment: 78 pages, 13 figure
A Lexicalized Tree-Adjoining Grammar for Vietnamese
In this paper, we present the first sizable grammar built for Vietnamese using LTAG, developed over the past two years, named vnLTAG. This grammar aims at modelling written language and is general enough to be both application- and domain-independent. It can be used for the morpho-syntactic tagging and syntactic parsing of Vietnamese texts, as well as text generation. We then present a robust parsing scheme using vnLTAG and a parser for the grammar. We finish with an evaluation using a test suite
Incremental Interpretation: Applications, Theory, and Relationship to Dynamic Semantics
Why should computers interpret language incrementally? In recent years
psycholinguistic evidence for incremental interpretation has become more and
more compelling, suggesting that humans perform semantic interpretation before
constituent boundaries, possibly word by word. However, possible computational
applications have received less attention. In this paper we consider various
potential applications, in particular graphical interaction and dialogue. We
then review the theoretical and computational tools available for mapping from
fragments of sentences to fully scoped semantic representations. Finally, we
tease apart the relationship between dynamic semantics and incremental
interpretation.Comment: Procs. of COLING 94, LaTeX (2.09 preferred), 8 page
An open source rule induction tool for transfer-based SMT
In this paper we describe an open source tool for automatic induction of transfer rules. Transfer rule induction is carried out on pairs of dependency structures and their node alignment to produce all rules consistent with the node alignment. We describe an efficient algorithm for rule induction and give a detailed description of how to use the tool
CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania
CLIFF is the Computational Linguists\u27 Feedback Forum. We are a group of students and faculty who gather once a week to hear a presentation and discuss work currently in progress. The \u27feedback\u27 in the group\u27s name is important: we are interested in sharing ideas, in discussing ongoing research, and in bringing together work done by the students and faculty in Computer Science and other departments.
However, there are only so many presentations which we can have in a year. We felt that it would be beneficial to have a report which would have, in one place, short descriptions of the work in Natural Language Processing at the University of Pennsylvania. This report then, is a collection of abstracts from both faculty and graduate students, in Computer Science, Psychology and Linguistics. We want to stress the close ties between these groups, as one of the things that we pride ourselves on here at Penn is the communication among different departments and the inter-departmental work.
Rather than try to summarize the varied work currently underway at Penn, we suggest reading the abstracts to see how the students and faculty themselves describe their work. The report illustrates the diversity of interests among the researchers here, as well as explaining the areas of common interest. In addition, since it was our intent to put together a document that would be useful both inside and outside of the university, we hope that this report will explain to everyone some of what we are about
Applications of Evolutionary Algorithms in Formal Languages
Starting from the model proposed by means of Grammatical Evolution, we extend the applicability of the parallel and cooperative searching processes of Evolutionary Algorithms to a new topic: Tree Adjoining Grammar parsing. We evolved derived trees using a string-tree-representation.We also used a linear matching function to compare the yield of a derived tree with a given input. The running tests presented several encouraging results. A post running analysis allowed us to propose several research directions for extending the currently known computational mechanisms in the mildly context sensitive class of languages
Probabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl
- …