5,516 research outputs found
Data-Oriented Language Processing. An Overview
During the last few years, a new approach to language processing has started
to emerge, which has become known under various labels such as "data-oriented
parsing", "corpus-based interpretation", and "tree-bank grammar" (cf. van den
Berg et al. 1994; Bod 1992-96; Bod et al. 1996a/b; Bonnema 1996; Charniak
1996a/b; Goodman 1996; Kaplan 1996; Rajman 1995a/b; Scha 1990-92; Sekine &
Grishman 1995; Sima'an et al. 1994; Sima'an 1995-96; Tugwell 1995). This
approach, which we will call "data-oriented processing" or "DOP", embodies the
assumption that human language perception and production works with
representations of concrete past language experiences, rather than with
abstract linguistic rules. The models that instantiate this approach therefore
maintain large corpora of linguistic representations of previously occurring
utterances. When processing a new input utterance, analyses of this utterance
are constructed by combining fragments from the corpus; the
occurrence-frequencies of the fragments are used to estimate which analysis is
the most probable one.
In this paper we give an in-depth discussion of a data-oriented processing
model which employs a corpus of labelled phrase-structure trees. Then we review
some other models that instantiate the DOP approach. Many of these models also
employ labelled phrase-structure trees, but use different criteria for
extracting fragments from the corpus or employ different disambiguation
strategies (Bod 1996b; Charniak 1996a/b; Goodman 1996; Rajman 1995a/b; Sekine &
Grishman 1995; Sima'an 1995-96); other models use richer formalisms for their
corpus annotations (van den Berg et al. 1994; Bod et al., 1996a/b; Bonnema
1996; Kaplan 1996; Tugwell 1995).Comment: 34 pages, Postscrip
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
From treebank resources to LFG F-structures
We present two methods for automatically annotating treebank resources with functional structures. Both methods define systematic patterns of correspondence between partial PS configurations and functional structures. These are applied to PS rules extracted from treebanks, or directly to constraint set encodings of treebank PS trees
Automatic acquisition of LFG resources for German - as good as it gets
We present data-driven methods for the acquisition of LFG resources from two German treebanks. We discuss problems specific to semi-free word order languages as well as problems arising fromthe data structures determined
by the design of the different treebanks. We compare two ways of encoding semi-free word order, as done in the two German treebanks, and argue that the design of the TiGer treebank is more adequate for the acquisition of LFG
resources. Furthermore, we describe an architecture for LFG grammar acquisition for German, based on the two German treebanks, and compare our results with a hand-crafted German LFG grammar
Evaluating two methods for Treebank grammar compaction
Treebanks, such as the Penn Treebank, provide a basis for the automatic creation of broad coverage grammars. In the simplest case, rules can simply be ‘read off’ the parse-annotations of the corpus, producing either a simple or probabilistic context-free grammar. Such grammars, however, can be very large, presenting problems for the subsequent computational costs of parsing under the grammar.
In this paper, we explore ways by which a treebank grammar can be reduced in size or ‘compacted’, which involve the use of two kinds of technique: (i) thresholding of rules by their number of occurrences; and (ii) a method of rule-parsing, which has both probabilistic and non-probabilistic variants. Our results show that by a combined use of these two techniques, a probabilistic context-free grammar can be reduced in size by 62% without any loss in parsing performance, and by 71% to give a gain in recall, but some loss in precision
Automatic F-Structure Annotation from the AP Treebank
We present a method for automatically annotating treebank resources with functional structures. The method defines systematic patterns of correspondence between partial PS configurations and functional structures. These are applied to PS rules extracted from treebanks. The set of techniques which we have developed constitute a methodology for corpus-guided grammar development. Despite the widespread belief that treebank representations are not very useful in grammar development, we show that systematic patterns of c-structure to f-structure correspondence can be simply and successfully stated over such rules. The method is partial in that it requires manual correction of the annotated grammar rules
Towards Neural Machine Translation with Latent Tree Attention
Building models that take advantage of the hierarchical structure of language
without a priori annotation is a longstanding goal in natural language
processing. We introduce such a model for the task of machine translation,
pairing a recurrent neural network grammar encoder with a novel attentional
RNNG decoder and applying policy gradient reinforcement learning to induce
unsupervised tree structures on both the source and target. When trained on
character-level datasets with no explicit segmentation or parse annotation, the
model learns a plausible segmentation and shallow parse, obtaining performance
close to an attentional baseline.Comment: Presented at SPNLP 201
A Data-Oriented Approach to Semantic Interpretation
In Data-Oriented Parsing (DOP), an annotated language corpus is used as a
stochastic grammar. The most probable analysis of a new input sentence is
constructed by combining sub-analyses from the corpus in the most probable way.
This approach has been succesfully used for syntactic analysis, using corpora
with syntactic annotations such as the Penn Treebank. If a corpus with
semantically annotated sentences is used, the same approach can also generate
the most probable semantic interpretation of an input sentence. The present
paper explains this semantic interpretation method, and summarizes the results
of a preliminary experiment. Semantic annotations were added to the syntactic
annotations of most of the sentences of the ATIS corpus. A data-oriented
semantic interpretation algorithm was succesfully tested on this semantically
enriched corpus.Comment: 10 pages, Postscript; to appear in Proceedings Workshop on
Corpus-Oriented Semantic Analysis, ECAI-96, Budapes
Treebank-based acquisition of wide-coverage, probabilistic LFG resources: project overview, results and evaluation
This paper presents an overview of a project to acquire wide-coverage, probabilistic Lexical-Functional Grammar
(LFG) resources from treebanks. Our approach is based on an automatic annotation algorithm that annotates “raw” treebank trees with LFG f-structure information approximating to basic predicate-argument/dependency structure. From the f-structure-annotated treebank
we extract probabilistic unification grammar resources. We present the annotation algorithm, the extraction of
lexical information and the acquisition of wide-coverage and robust PCFG-based LFG approximations including
long-distance dependency resolution.
We show how the methodology can be applied to multilingual, treebank-based unification grammar acquisition. Finally
we show how simple (quasi-)logical forms can be derived automatically from the f-structures generated for the treebank trees
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