13,001 research outputs found
The Synonym management process in SAREL
The specification phase is one of the most important and least supported
parts of the software development process. The SAREL system has been
conceived as a knowledge-based tool to improve the specification phase.
The purpose of SAREL (Assistance System for Writing Software
Specifications in Natural Language) is to assist engineers in the
creation of software specifications written in Natural Language (NL).
These documents are divided into several parts. We can distinguish the
Introduction and the Overall Description as parts that should be used in
the Knowledge Base construction. The information contained in the
Specific Requirements Section corresponds to the information represented
in the Requirements Base. In order to obtain high-quality software
requirements specification the writing norms that define the linguistic
restrictions required and the software engineering constraints related
to the quality factors have been taken into account. One of the controls
performed is the lexical analysis that verifies the words belong to the
application domain lexicon which consists of the Required and the
Extended lexicon. In this sense a synonym management process is needed
in order to get a quality software specification. The aim of this paper
is to present the synonym management process performed during the
Knowledge Base construction. Such process makes use of the Spanish
Wordnet developed inside the Eurowordnet project. This process generates
both the Required lexicon and the Extended lexicon that will be used
during the Requirements Base construction.Postprint (published version
Using NLP tools in the specification phase
The software quality control is one of the main topics in the Software
Engineering area. To put the effort in the quality control during the
specification phase leads us to detect possible mistakes in an early
steps and, easily, to correct them before the design and implementation
steps start. In this framework the goal of SAREL system, a
knowledge-based system, is twofold. On one hand, to help software
engineers in the creation of quality Software Requirements
Specifications. On the other hand, to analyze the correspondence between
two different conceptual representations associated with two different
Software Requirements Specification documents.
For the first goal, a set of NLP and Knowledge management tools is
applied to obtain a conceptual representation that can be validated and
managed by the software engineer.
For the second goal we have established some correspondence measures in
order to get a comparison between two conceptual representations. This
information will be useful during the interaction.Postprint (published version
Lexical typology : a programmatic sketch
The present paper is an attempt to lay the foundation for Lexical Typology as a new kind of linguistic typology.1 The goal of Lexical Typology is to investigate crosslinguistically significant patterns of interaction between lexicon and grammar
Acquiring Word-Meaning Mappings for Natural Language Interfaces
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance
Having Your Cake and Eating It Too: Autonomy and Interaction in a Model of Sentence Processing
Is the human language understander a collection of modular processes
operating with relative autonomy, or is it a single integrated process? This
ongoing debate has polarized the language processing community, with two
fundamentally different types of model posited, and with each camp concluding
that the other is wrong. One camp puts forth a model with separate processors
and distinct knowledge sources to explain one body of data, and the other
proposes a model with a single processor and a homogeneous, monolithic
knowledge source to explain the other body of data. In this paper we argue that
a hybrid approach which combines a unified processor with separate knowledge
sources provides an explanation of both bodies of data, and we demonstrate the
feasibility of this approach with the computational model called COMPERE. We
believe that this approach brings the language processing community
significantly closer to offering human-like language processing systems.Comment: 7 pages, uses aaai.sty macr
Morphological Productivity in the Lexicon
In this paper we outline a lexical organization for Turkish that makes use of
lexical rules for inflections, derivations, and lexical category changes to
control the proliferation of lexical entries. Lexical rules handle changes in
grammatical roles, enforce type constraints, and control the mapping of
subcategorization frames in valency-changing operations. A lexical inheritance
hierarchy facilitates the enforcement of type constraints. Semantic
compositions in inflections and derivations are constrained by the properties
of the terms and predicates.
The design has been tested as part of a HPSG grammar for Turkish. In terms of
performance, run-time execution of the rules seems to be a far better
alternative than pre-compilation. The latter causes exponential growth in the
lexicon due to intensive use of inflections and derivations in Turkish.Comment: 10 pages LaTeX, {lingmacros,avm,psfig}.sty, 1 figure, 1 bibtex fil
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