27,247 research outputs found
GEMINI: A Natural Language System for Spoken-Language Understanding
Gemini is a natural language understanding system developed for spoken
language applications. The paper describes the architecture of Gemini, paying
particular attention to resolving the tension between robustness and
overgeneration. Gemini features a broad-coverage unification-based grammar of
English, fully interleaved syntactic and semantic processing in an all-paths,
bottom-up parser, and an utterance-level parser to find interpretations of
sentences that might not be analyzable as complete sentences. Gemini also
includes novel components for recognizing and correcting grammatical
disfluencies, and for doing parse preferences. This paper presents a
component-by-component view of Gemini, providing detailed relevant measurements
of size, efficiency, and performance.Comment: 8 pages, postscrip
ON MONITORING LANGUAGE CHANGE WITH THE SUPPORT OF CORPUS PROCESSING
One of the fundamental characteristics of language is that it can change over time. One
method to monitor the change is by observing its corpora: a structured language
documentation. Recent development in technology, especially in the field of Natural
Language Processing allows robust linguistic processing, which support the description of
diverse historical changes of the corpora. The interference of human linguist is inevitable as
it determines the gold standard, but computer assistance provides considerable support by
incorporating computational approach in exploring the corpora, especially historical
corpora. This paper proposes a model for corpus development, where corpus are annotated
to support further computational operations such as lexicogrammatical pattern matching,
automatic retrieval and extraction. The corpus processing operations are performed by local
grammar based corpus processing software on a contemporary Indonesian corpus. This
paper concludes that data collection and data processing in a corpus are equally crucial
importance to monitor language change, and none can be set aside
Combining semantic and syntactic structure for language modeling
Structured language models for speech recognition have been shown to remedy
the weaknesses of n-gram models. All current structured language models are,
however, limited in that they do not take into account dependencies between
non-headwords. We show that non-headword dependencies contribute to
significantly improved word error rate, and that a data-oriented parsing model
trained on semantically and syntactically annotated data can exploit these
dependencies. This paper also contains the first DOP model trained by means of
a maximum likelihood reestimation procedure, which solves some of the
theoretical shortcomings of previous DOP models.Comment: 4 page
Robust semantic analysis for adaptive speech interfaces
The DUMAS project develops speech-based applications that are adaptable to different users and domains. The paper describes the project's robust semantic analysis strategy, used both in the generic framework for the development of multilingual speech-based dialogue systems which is the main project goal, and in the initial test application, a mobile phone-based e-mail interface
Chart-driven Connectionist Categorial Parsing of Spoken Korean
While most of the speech and natural language systems which were developed
for English and other Indo-European languages neglect the morphological
processing and integrate speech and natural language at the word level, for the
agglutinative languages such as Korean and Japanese, the morphological
processing plays a major role in the language processing since these languages
have very complex morphological phenomena and relatively simple syntactic
functionality. Obviously degenerated morphological processing limits the usable
vocabulary size for the system and word-level dictionary results in exponential
explosion in the number of dictionary entries. For the agglutinative languages,
we need sub-word level integration which leaves rooms for general morphological
processing. In this paper, we developed a phoneme-level integration model of
speech and linguistic processings through general morphological analysis for
agglutinative languages and a efficient parsing scheme for that integration.
Korean is modeled lexically based on the categorial grammar formalism with
unordered argument and suppressed category extensions, and chart-driven
connectionist parsing method is introduced.Comment: 6 pages, Postscript file, Proceedings of ICCPOL'9
Evaluation of an automatic f-structure annotation algorithm against the PARC 700 dependency bank
An automatic method for annotating the Penn-II Treebank (Marcus et al., 1994) with high-level Lexical Functional Grammar (Kaplan and Bresnan, 1982; Bresnan, 2001; Dalrymple, 2001) f-structure representations is described in (Cahill et al., 2002; Cahill et al., 2004a; Cahill et al., 2004b; OâDonovan et al., 2004). The annotation algorithm and the automatically-generated f-structures are the basis for the automatic acquisition of wide-coverage and robust probabilistic approximations of LFG grammars (Cahill et al., 2002; Cahill et al., 2004a) and for the induction of LFG semantic forms (OâDonovan et al., 2004). The quality of the annotation algorithm and the f-structures it generates is, therefore, extremely important. To date, annotation quality has been measured in terms of precision and recall against the DCU 105. The annotation algorithm currently achieves an f-score of 96.57% for complete f-structures and 94.3% for preds-only
f-structures. There are a number of problems with evaluating against a gold standard of this size, most
notably that of overfitting. There is a risk of assuming that the gold standard is a complete and balanced
representation of the linguistic phenomena in a language and basing design decisions on this. It is, therefore,
preferable to evaluate against a more extensive, external standard. Although the DCU 105 is publicly available,
1 a larger well-established external standard can provide a more widely-recognised benchmark against which the quality of the f-structure annotation algorithm can be evaluated. For these reasons, we present an evaluation of the f-structure annotation algorithm of (Cahill et al., 2002; Cahill et al., 2004a; Cahill et al., 2004b; OâDonovan et al., 2004) against the PARC 700 Dependency Bank (King et al., 2003). Evaluation against an external gold standard is a non-trivial task as linguistic analyses may differ systematically between the gold standard and the output to be evaluated as regards feature geometry and nomenclature. We present conversion software to automatically account for many (but not all) of the systematic differences. Currently, we achieve an f-score of 87.31% for the f-structures generated from the original Penn-II trees and
an f-score of 81.79% for f-structures from parse trees produced by Charniakâs (2000) parser in our pipeline
parsing architecture against the PARC 700
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