ABL: Alignment-Based Learning
This paper introduces a new type of grammar learning algorithm, inspired by string edit distance (Wagner and Fischer, 1974). The
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A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation
This paper describes a set of comparative experiments, including cross-corpus evaluation, between five alternative algorithms for supervised Word Sense Disambiguation
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A Distributed Multimedia Communication System and its Applications to E-Learning
In this paper we report on a multimedia communication system including a VCoIP (Video Conferencing over IP) software with a
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A Learning Algorithm for Evolving Cascade Neural Networks
A new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is described. An ECNN starts to learn with one input
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A Learning Approach to Shallow Parsing
A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic
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A Machine-Learning Approach to Estimating the Referential Properties of Japanese Noun Phrases
The referential properties of noun phrases in the Japanese language, which has no articles, are useful for article generation in
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A Memory-Based Approach to Learning Shallow Natural Language Patterns
Recognizing shallow linguistic patterns, such as basic syntactic relationships between words, is a common task in applied natural language and
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A Neural Network Decision Tree for Learning Concepts from EEG Data
To learn the multi-class conceptions from the electroencephalogram (EEG) data we developed a neural network decision tree (DT), that performs
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An Investigation of Transformation-Based Learning in Discourse
This paper presents results from the first attempt to apply Transformation-Based Learning to a discourse-level Natural Language Processing task. To
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A novel stochastic Hebb-like learning rule for neural networks
We present a novel stochastic Hebb-like learning rule for neural networks. This learning rule is stochastic with respect to the
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Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis
We describe how specialized database technology and data analysis methods were applied by the Swedish defense to help deal with
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Applying Explanation-based Learning to Control and Speeding-up Natural Language Generation
This paper presents a method for the automatic extraction of subgrammars to control and speeding-up natural language generation NLG. The
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A procedure for unsupervised lexicon learning
We describe an incremental unsupervised procedure to learn words from transcribed continuous speech. The algorithm is based on a conservative
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A Semantic Grid-based E-Learning Framework (SELF)
E-learning can be loosely defined as a wide set of applications and processes, which uses available electronic media (and tools)
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Automated Determination of Stellar Population Parameters in Galaxies Using Active Instance-based Learning
In this work we focus on the determination of the relative distributions of young, intermediate-age and old populations of stars
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Bidirectional communication in neural networks moderated by a Hebb-like learning rule
We demonstrate that our recently introduced stochastic Hebb-like learning rule is capable of learning the problem of timing in general
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Bootstrapping Structure into Language: Alignment-Based Learning
This thesis introduces a new unsupervised learning framework, called Alignment-Based Learning, which is based on the alignment of sentences and
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Cluster Diffusion and Coalescence on Metal Surfaces: applications of a Self-learning Kinetic Monte-Carlo method
The Kinetic Monte Carlo (KMC) method has become an important tool for examination of phenomena like surface diffusion and thin
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Combining a self-organising map with memory-based learning
Memory-based learning (MBL) has enjoyed considerable success in corpus-based natural language processing (NLP) tasks and is thus a reliable method
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Combining Hebbian and reinforcement learning in a minibrain model
A toy model of a neural network in which both Hebbian learning and reinforcement learning occur is studied. The problem
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Competition between adaptive agents: from learning to collective efficiency and back
We use the Minority Game and some of its variants to show how efficiency depends on learning in models of
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Competitive on-line learning with a convex loss function
We consider the problem of sequential decision making under uncertainty in which the loss caused by a decision depends on
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Computing Dialogue Acts from Features with Transformation-Based Learning
To interpret natural language at the discourse level, it is very useful to accurately recognize dialogue acts, such as SUGGEST,
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Content-Based Book Recommending Using Learning for Text Categorization
Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's
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