Book ChapterDOI
Context Vectors: A Step Toward a "Grand Unified Representation"
Stephen I. Gallant
- pp 204-210
TLDR
Accommodating all these capabilities into a “Grand Unified Representation” is, it is maintained, a prerequisite for solving the most difficult problems in Artificial Intelligence, including natural language understanding.Abstract:
Context Vectors are fixed-length vector representations useful for document retrieval and word sense disambiguation. Context vectors were motivated by four goals:
1
Capture “similarity of use” among words (“car” is similar to “auto”, but not similar to “hippopotamus”).
2
Quickly find constituent objects (eg., documents that contain specified words).
3
Generate context vectors automatically from an unlabeled corpus.
4
Use context vectors as input to standard learning algorithms.
Context Vectors lack, however, a natural way to represent syntax, discourse, or logic. Accommodating all these capabilities into a “Grand Unified Representation” is, we maintain, a prerequisite for solving the most difficult problems in Artificial Intelligence, including natural language understanding.read more
Citations
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Dissertation
The Word-Space Model : Using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces
TL;DR: The word-space model is a computational model of word meaning that utilizes the distributional patterns of words collected over large text data to represent semantic similarity between words in terabytes of data.
BookDOI
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TL;DR: An overview of Hybrid Neural Systems and Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks are presented.
Reflective Random Indexing and Indirect Inference: A Scalable Method for Discovery of
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Journal ArticleDOI
Reflective Random Indexing and indirect inference: A scalable method for discovery of implicit connections
TL;DR: The authors evaluate the ability of Random Indexing (RI), a scalable distributional model of word associations, to draw meaningful implicit relationships between terms in general and biomedical language, and demonstrate that the original implementation of RI is ineffective at inferring meaningful indirect connections.
Book ChapterDOI
An Overview of Hybrid Neural Systems
Stefan Wermter,Ron Sun +1 more
TL;DR: This chapter provides an introduction to the field of hybrid neural systems, giving a brief overview of the main methods used, outline the work that is presented here, and provide additional references.
References
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TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
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TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
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Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
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TL;DR: This text and reference provides a systematic development of neural network learning algorithms from a computational perspective, coupled with an extensive exploration of Neural Network expert systems which shows how the power of neuralnetwork learning can be harnessed to generate expert systems automatically.
Journal ArticleDOI
Improving the retrieval of information from external sources
TL;DR: A statistical method is described called latent semantic indexing, which models the implicit higher order structure in the association of words and objects and improves retrieval performance by up to 30%.
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