1,541 research outputs found
Inducing Constraint Grammars
Constraint Grammar rules are induced from corpora. A simple scheme based on
local information, i.e., on lexical biases and next-neighbour contexts,
extended through the use of barriers, reached 87.3 percent precision (1.12
tags/word) at 98.2 percent recall. The results compare favourably with other
methods that are used for similar tasks although they are by no means as good
as the results achieved using the original hand-written rules developed over
several years time.Comment: 10 pages, uuencoded, gzipped PostScrip
A comparison of parsing technologies for the biomedical domain
This paper reports on a number of experiments which are designed to investigate the extent to which current nlp resources are able to syntactically and semantically analyse biomedical text. We address two tasks: parsing a real corpus with a hand-built widecoverage grammar, producing both syntactic analyses and logical forms; and automatically computing the interpretation of compound nouns where the head is a nominalisation (e.g., hospital arrival means an arrival at hospital, while patient arrival means an arrival of a patient). For the former task we demonstrate that exible and yet constrained `preprocessing ' techniques are crucial to success: these enable us to use part-of-speech tags to overcome inadequate lexical coverage, and to `package up' complex technical expressions prior to parsing so that they are blocked from creating misleading amounts of syntactic complexity. We argue that the xml-processing paradigm is ideally suited for automatically preparing the corpus for parsing. For the latter task, we compute interpretations of the compounds by exploiting surface cues and meaning paraphrases, which in turn are extracted from the parsed corpus. This provides an empirical setting in which we can compare the utility of a comparatively deep parser vs. a shallow one, exploring the trade-o between resolving attachment ambiguities on the one hand and generating errors in the parses on the other. We demonstrate that a model of the meaning of compound nominalisations is achievable with the aid of current broad-coverage parsers
Robust Grammatical Analysis for Spoken Dialogue Systems
We argue that grammatical analysis is a viable alternative to concept
spotting for processing spoken input in a practical spoken dialogue system. We
discuss the structure of the grammar, and a model for robust parsing which
combines linguistic sources of information and statistical sources of
information. We discuss test results suggesting that grammatical processing
allows fast and accurate processing of spoken input.Comment: Accepted for JNL
Chunk Tagger - Statistical Recognition of Noun Phrases
We describe a stochastic approach to partial parsing, i.e., the recognition
of syntactic structures of limited depth. The technique utilises Markov Models,
but goes beyond usual bracketing approaches, since it is capable of recognising
not only the boundaries, but also the internal structure and syntactic category
of simple as well as complex NP's, PP's, AP's and adverbials. We compare
tagging accuracy for different applications and encoding schemes.Comment: 7 pages, LaTe
CINTIL DependencyBank PREMIUM. A corpus of grammatical dependencies for Portuguese
This paper presents a new linguistic resource for the study and computational processing of Portuguese. CINTIL DependencyBank PREMIUM is a corpus of Portuguese news text, accurately manually annotated with a wide range of linguistic information (morpho-syntax, named-entities, syntactic function and semantic roles), making it an invaluable resource specially for the development and evaluation of data-driven natural language processing tools. The corpus is under active development, reaching 4,000 sentences in its current version. The paper also reports on the training and evaluation of a dependency parser over this corpus. CINTIL DependencyBank PREMIUM is freely-available for research purposes through META-SHARE.info:eu-repo/semantics/publishedVersio
Bayesian Information Extraction Network
Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various
aspects of language in one model. Many existing algorithms developed for
learning and inference in DBNs are applicable to probabilistic language
modeling. To demonstrate the potential of DBNs for natural language processing,
we employ a DBN in an information extraction task. We show how to assemble
wealth of emerging linguistic instruments for shallow parsing, syntactic and
semantic tagging, morphological decomposition, named entity recognition etc. in
order to incrementally build a robust information extraction system. Our method
outperforms previously published results on an established benchmark domain.Comment: 6 page
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