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Toward an Architecture for Never-Ending Language Learning
TLDR
This work proposes an approach and a set of design principles for an intelligent computer agent that runs forever and describes a partial implementation of such a system that has already learned to extract a knowledge base containing over 242,000 beliefs. Expand
Text Classification from Labeled and Unlabeled Documents using EM
TLDR
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents, and presents two extensions to the algorithm that improve classification accuracy under these conditions. Expand
Predicting Human Brain Activity Associated with the Meanings of Nouns
TLDR
A computational model is presented that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available, trained with a combination of data from a trillion-word text corpus and observed f MRI data associated with viewing several dozen concrete nouns. Expand
Machine learning classifiers and fMRI: A tutorial overview
TLDR
This tutorial overview shows how, in addition to answering the question of 'is there information about a variable of interest' ( pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' and 'how is that information encoded' (pattern characterization). Expand
Generalization as Search
Abstract The problem of concept learning, or forming a general description of a class of objects given a set of examples and non-examples, is viewed here as a search problem. Existing programs thatExpand
Random Walk Inference and Learning in A Large Scale Knowledge Base
TLDR
It is shown that a soft inference procedure based on a combination of constrained, weighted, random walks through the knowledge base graph can be used to reliably infer new beliefs for theknowledge base. Expand
Learning to Extract Symbolic Knowledge from the World Wide Web
TLDR
The goal of the research described here is to automatically create a computer understandable world wide knowledge base whose content mirrors that of the World Wide Web, and several machine learning algorithms for this task are described. Expand
Zero-shot Learning with Semantic Output Codes
TLDR
A semantic output code classifier which utilizes a knowledge base of semantic properties of Y to extrapolate to novel classes and can often predict words that people are thinking about from functional magnetic resonance images of their neural activity, even without training examples for those words. Expand
Never-Ending Learning
TLDR
The Never-Ending Language Learner is described, which achieves some of the desired properties of a never-ending learner, and lessons learned are discussed. Expand
Coupled semi-supervised learning for information extraction
TLDR
This paper characterize several ways in which the training of category and relation extractors can be coupled, and presents experimental results demonstrating significantly improved accuracy as a result. Expand
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