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Author: Avi Bleiweiss

Affiliation: BShalem Research, United States

ISBN: 978-989-758-220-2

Keyword(s): Word Vectors, Deep Learning, Semantic Matching, Multidimensional Scaling, Clustering.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Methodologies and Methods ; Natural Language Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods ; Visualization

Abstract: Semantic word embeddings have shown to cluster in space based on linguistic similarities that are quantifiably captured using simple vector arithmetic. Recently, methods for learning distributed word vectors have progressively empowered neural language models to compute compositional vector representations for phrases of variable length. However, they remain limited in expressing more generic relatedness between instances of a larger and non-uniform sized body-of-text. In this work, we propose a formulation that combines a word vector set of variable cardinality to represent a verse or a sentence, with an iterative distance metric to evaluate similarity in pairs of non-conforming verse matrices. In contrast to baselines characterized by a bag of features, our model preserves word order and is more sustainable in performing semantic matching at any of a verse, chapter and book levels. Using our framework to train word vectors, we analyzed the clustering of bible books exploring multidi mensional scaling for visualization, and experimented with book searches of both contiguous and out-of-order parts of verses. We report robust results that support our intuition for measuring book-to-book and verse-to-book similarity. (More)

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Paper citation in several formats:
Bleiweiss A. (2017). A Hierarchical Book Representation of Word Embeddings for Effective Semantic Clustering and Search.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 154-163. DOI: 10.5220/0006192701540163

@conference{icaart17,
author={Avi Bleiweiss},
title={A Hierarchical Book Representation of Word Embeddings for Effective Semantic Clustering and Search},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={154-163},
publisher={ScitePress},
organization={INSTICC},
doi={10.5220/0006192701540163},
isbn={978-989-758-220-2},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Hierarchical Book Representation of Word Embeddings for Effective Semantic Clustering and Search
SN - 978-989-758-220-2
AU - Bleiweiss A.
PY - 2017
SP - 154
EP - 163
DO - 10.5220/0006192701540163

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