205 research outputs found
Sense Tagging: Semantic Tagging with a Lexicon
Sense tagging, the automatic assignment of the appropriate sense from some
lexicon to each of the words in a text, is a specialised instance of the
general problem of semantic tagging by category or type. We discuss which
recent word sense disambiguation algorithms are appropriate for sense tagging.
It is our belief that sense tagging can be carried out effectively by combining
several simple, independent, methods and we include the design of such a
tagger. A prototype of this system has been implemented, correctly tagging 86%
of polysemous word tokens in a small test set, providing evidence that our
hypothesis is correct.Comment: 6 pages, uses aclap LaTeX style file. Also in Proceedings of the
SIGLEX Workshop "Tagging Text with Lexical Semantics
New Methods, Current Trends and Software Infrastructure for NLP
The increasing use of `new methods' in NLP, which the NeMLaP conference
series exemplifies, occurs in the context of a wider shift in the nature and
concerns of the discipline. This paper begins with a short review of this
context and significant trends in the field. The review motivates and leads to
a set of requirements for support software of general utility for NLP research
and development workers. A freely-available system designed to meet these
requirements is described (called GATE - a General Architecture for Text
Engineering). Information Extraction (IE), in the sense defined by the Message
Understanding Conferences (ARPA \cite{Arp95}), is an NLP application in which
many of the new methods have found a home (Hobbs \cite{Hob93}; Jacobs ed.
\cite{Jac92}). An IE system based on GATE is also available for research
purposes, and this is described. Lastly we review related work.Comment: 12 pages, LaTeX, uses nemlap.sty (included
Ontologies, taxonomies, thesauri:learning from texts
The use of ontologies as representations of knowledge is widespread but their construction, until recently, has been entirely manual. We argue in this paper for the use of text corpora and automated natural language processing methods for the construction of ontologies. We delineate the challenges and present criteria for the selection of appropriate methods. We distinguish three ma jor steps in ontology building: associating terms, constructing hierarchies and labelling relations. A number of methods are presented for these purposes but we conclude that the issue of data-sparsity still is a ma jor challenge. We argue for the use of resources external tot he domain specific corpus
Compacting the Penn Treebank Grammar
Treebanks, such as the Penn Treebank (PTB), offer a simple approach to
obtaining a broad coverage grammar: one can simply read the grammar off the
parse trees in the treebank. While such a grammar is easy to obtain, a
square-root rate of growth of the rule set with corpus size suggests that the
derived grammar is far from complete and that much more treebanked text would
be required to obtain a complete grammar, if one exists at some limit. However,
we offer an alternative explanation in terms of the underspecification of
structures within the treebank. This hypothesis is explored by applying an
algorithm to compact the derived grammar by eliminating redundant rules --
rules whose right hand sides can be parsed by other rules. The size of the
resulting compacted grammar, which is significantly less than that of the full
treebank grammar, is shown to approach a limit. However, such a compacted
grammar does not yield very good performance figures. A version of the
compaction algorithm taking rule probabilities into account is proposed, which
is argued to be more linguistically motivated. Combined with simple
thresholding, this method can be used to give a 58% reduction in grammar size
without significant change in parsing performance, and can produce a 69%
reduction with some gain in recall, but a loss in precision.Comment: 5 pages, 2 figure
Software Infrastructure for Natural Language Processing
We classify and review current approaches to software infrastructure for
research, development and delivery of NLP systems. The task is motivated by a
discussion of current trends in the field of NLP and Language Engineering. We
describe a system called GATE (a General Architecture for Text Engineering)
that provides a software infrastructure on top of which heterogeneous NLP
processing modules may be evaluated and refined individually, or may be
combined into larger application systems. GATE aims to support both researchers
and developers working on component technologies (e.g. parsing, tagging,
morphological analysis) and those working on developing end-user applications
(e.g. information extraction, text summarisation, document generation, machine
translation, and second language learning). GATE promotes reuse of component
technology, permits specialisation and collaboration in large-scale projects,
and allows for the comparison and evaluation of alternative technologies. The
first release of GATE is now available - see
http://www.dcs.shef.ac.uk/research/groups/nlp/gate/Comment: LaTeX, uses aclap.sty, 8 page
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