134,064 research outputs found
CADgpt: Harnessing Natural Language Processing for 3D Modelling to Enhance Computer-Aided Design Workflows
This paper introduces CADgpt, an innovative plugin integrating Natural
Language Processing (NLP) with Rhino3D for enhancing 3D modelling in
computer-aided design (CAD) environments. Leveraging OpenAI's GPT-4, CADgpt
simplifies the CAD interface, enabling users, particularly beginners, to
perform complex 3D modelling tasks through intuitive natural language commands.
This approach significantly reduces the learning curve associated with
traditional CAD software, fostering a more inclusive and engaging educational
environment. The paper discusses CADgpt's technical architecture, including its
integration within Rhino3D and the adaptation of GPT-4 capabilities for CAD
tasks. It presents case studies demonstrating CADgpt's efficacy in various
design scenarios, highlighting its potential to democratise design education by
making sophisticated design tools accessible to a broader range of students.
The discussion further explores CADgpt's implications for pedagogy and
curriculum development, emphasising its role in enhancing creative exploration
and conceptual thinking in design education.
Keywords: Natural Language Processing, Computer-Aided Design, 3D Modelling,
Design Automation, Design Education, Architectural EducationComment: 10 pages, 4 figure
Experimental Support of Argument-based Syntactic Computation
Linguistic theory, cognitive, information, and mathematical modeling are all useful while we attempt to
achieve a better understanding of the Language Faculty (LF). This cross-disciplinary approach will eventually
lead to the identification of the key principles applicable in the systems of Natural Language Processing. The
present work concentrates on the syntax-semantics interface. We start from recursive definitions and application
of optimization principles, and gradually develop a formal model of syntactic operations. The result – a Fibonacci-
like syntactic tree – is in fact an argument-based variant of the natural language syntax. This representation
(argument-centered model, ACM) is derived by a recursive calculus that generates a mode which connects
arguments and expresses relations between them. The reiterative operation assigns primary role to entities as
the key components of syntactic structure. We provide experimental evidence in support of the argument-based
model. We also show that mental computation of syntax is influenced by the inter-conceptual relations between
the images of entities in a semantic space
Natural language querying for video databases
Cataloged from PDF version of article.The video databases have become popular in various areas due to the recent advances in technology. Video archive systems need user-friendly interfaces to retrieve video frames. In this paper, a user interface based on natural language processing (NLP) to a video database system is described. The video database is based on a content-based spatio-temporal video data model. The data model is focused on the semantic content which includes objects, activities, and spatial properties of objects. Spatio-temporal relationships between video objects and also trajectories of moving objects can be queried with this data model. In this video database system, a natural language interface enables flexible querying. The queries, which are given as English sentences, are parsed using link parser. The semantic representations of the queries are extracted from their syntactic structures using information extraction techniques. The extracted semantic representations are used to call the related parts of the underlying video database system to return the results of the queries. Not only exact matches but similar objects and activities are also returned from the database with the help of the conceptual ontology module. This module is implemented using a distance-based method of semantic similarity search on the semantic domain-independent ontology, WordNet. (C) 2008 Elsevier Inc. All rights reserved
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
Knowledge Rich Natural Language Queries over Structured Biological Databases
Increasingly, keyword, natural language and NoSQL queries are being used for
information retrieval from traditional as well as non-traditional databases
such as web, document, image, GIS, legal, and health databases. While their
popularity are undeniable for obvious reasons, their engineering is far from
simple. In most part, semantics and intent preserving mapping of a well
understood natural language query expressed over a structured database schema
to a structured query language is still a difficult task, and research to tame
the complexity is intense. In this paper, we propose a multi-level
knowledge-based middleware to facilitate such mappings that separate the
conceptual level from the physical level. We augment these multi-level
abstractions with a concept reasoner and a query strategy engine to dynamically
link arbitrary natural language querying to well defined structured queries. We
demonstrate the feasibility of our approach by presenting a Datalog based
prototype system, called BioSmart, that can compute responses to arbitrary
natural language queries over arbitrary databases once a syntactic
classification of the natural language query is made
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
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