23,848 research outputs found
MBT: A Memory-Based Part of Speech Tagger-Generator
We introduce a memory-based approach to part of speech tagging. Memory-based
learning is a form of supervised learning based on similarity-based reasoning.
The part of speech tag of a word in a particular context is extrapolated from
the most similar cases held in memory. Supervised learning approaches are
useful when a tagged corpus is available as an example of the desired output of
the tagger. Based on such a corpus, the tagger-generator automatically builds a
tagger which is able to tag new text the same way, diminishing development time
for the construction of a tagger considerably. Memory-based tagging shares this
advantage with other statistical or machine learning approaches. Additional
advantages specific to a memory-based approach include (i) the relatively small
tagged corpus size sufficient for training, (ii) incremental learning, (iii)
explanation capabilities, (iv) flexible integration of information in case
representations, (v) its non-parametric nature, (vi) reasonably good results on
unknown words without morphological analysis, and (vii) fast learning and
tagging. In this paper we show that a large-scale application of the
memory-based approach is feasible: we obtain a tagging accuracy that is on a
par with that of known statistical approaches, and with attractive space and
time complexity properties when using {\em IGTree}, a tree-based formalism for
indexing and searching huge case bases.} The use of IGTree has as additional
advantage that optimal context size for disambiguation is dynamically computed.Comment: 14 pages, 2 Postscript figure
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
Web-based CBR (case-based reasoning) as a tool with the application to tooling selection
Over the past few years, manufacturing companies
have had to deal with an increasing demand for feature-rich products at low costs. The pressures exerted on their existing manufacturing processes have lead manufacturers to investigate internet-based solutions, in order to cope with growing competition. The decentralisation phenomenon also came up as a reason to implement networked-application, which has been the starting point for internet/intranet–based systems. Today, the availability of powerful and low cost 3D tools, database backend systems, along with web-based technologies, provides interesting opportunities
to the manufacturing community, with solutions directly implementable at the core of their businesses and organisations. In this paper a web-based engineering approach is presented to developing a design support system using case-based reasoning (CBR) technology for helping in the decision-making process when choosing cutting tools. The system aims to provide on-line intelligent support for determining the most suitable configuration for turning operations, based on initial parameters and requirements for the cutting operation. The system also features a user-driven 3D turning simulator which allows testing the chosen insert for several turning operations. The system aims to be a useful e-manufacturing tool being able to quickly and responsively provide tooling data in a highly interactive way
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
A Theory of the Acquisition of Episodic Memory
Case-based reasoning (CBR) has been viewed by many as just a methodology for building systems, but the foundations of CBR are psychological theories. Dynamic Memory (Schank, 1982) was the first attempt to describe a theory for learning in computers and people, based on particular forms of data structures and processes, that nowadays are widely used in a variety of forms in CBR. In addition to being useful for system building, CBR provides a way of discussing a range of issues concerned with cognition. This focus on the practical uses of CBR has deflected attention from the need to develop further the underlying theory. In particular, the issue of knowledge acquisition, in not adequately handled by the existing theory. This paper discusses this theoretical weakness and then proposes an enhanced model of learning which is compatible with the CBR paradigm
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
We propose a computational model of situated language comprehension based on
the Indexical Hypothesis that generates meaning representations by translating
amodal linguistic symbols to modal representations of beliefs, knowledge, and
experience external to the linguistic system. This Indexical Model incorporates
multiple information sources, including perceptions, domain knowledge, and
short-term and long-term experiences during comprehension. We show that
exploiting diverse information sources can alleviate ambiguities that arise
from contextual use of underspecific referring expressions and unexpressed
argument alternations of verbs. The model is being used to support linguistic
interactions in Rosie, an agent implemented in Soar that learns from
instruction.Comment: Advances in Cognitive Systems 3 (2014
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