3,478 research outputs found
Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean
A new tightly coupled speech and natural language integration model is
presented for a TDNN-based continuous possibly large vocabulary speech
recognition system for Korean. Unlike popular n-best techniques developed for
integrating mainly HMM-based speech recognition and natural language processing
in a {\em word level}, which is obviously inadequate for morphologically
complex agglutinative languages, our model constructs a spoken language system
based on a {\em morpheme-level} speech and language integration. With this
integration scheme, the spoken Korean processing engine (SKOPE) is designed and
implemented using a TDNN-based diphone recognition module integrated with a
Viterbi-based lexical decoding and symbolic phonological/morphological
co-analysis. Our experiment results show that the speaker-dependent continuous
{\em eojeol} (Korean word) recognition and integrated morphological analysis
can be achieved with over 80.6% success rate directly from speech inputs for
the middle-level vocabularies.Comment: latex source with a4 style, 15 pages, to be published in computer
processing of oriental language journa
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
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
PARSEC: A Constraint-Based Parser for Spoken Language Processing
PARSEC (1), a text-based and spoken language processing framework based on the Constraint Dependency Grammar (CDG) developed by Maruyama [26,27], is discussed. The scope of CDG is expanded to allow for the analysis of sentences containing lexically ambiguous words, to allow feature analysis in constraints, and to efficiently process multiple sentence candidates that are likely to arise in spoken language processing. The benefits of the CDG parsing approach are summarized. Additionally, the development CDG grammars using PARSEC grammar writing tools and the implementation of the PARSEC parser for word graphs is discussed. (1) Parallel ARchitecture Sentence Constraine
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