Enhancing clinical concept extraction with distributional semantics
Journal of Biomedical Informatics
Exploration of affect detection using semantic cues in virtual improvisation
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Discovering discovery patterns with predication-based Semantic Indexing
Journal of Biomedical Informatics
Contextual and active learning-based affect-sensing from virtual drama improvisation
ACM Transactions on Speech and Language Processing (TSLP)
Real, complex, and binary semantic vectors
QI'12 Proceedings of the 6th international conference on Quantum Interaction
A scalable server for key distribution and its application to accounting
The Journal of Supercomputing
Affect detection from text-based virtual improvisation and emotional gesture recognition
Advances in Human-Computer Interaction
Towards a Semantic-Based Approach for Affect and Metaphor Detection
International Journal of Distance Education Technologies
Escaping the trap of too precise topic queries
CICM'13 Proceedings of the 2013 international conference on Intelligent Computer Mathematics
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Distributional semantics is the branch of natural language processing that attempts to model the meanings of words, phrases and documents from the distribution and usage of words in a corpus of text. In the past three years, research in this area has been accelerated by the availability of the Semantic Vectors package, a stable, fast, scalable, and free software package for creating and exploring concepts in distributional models. This paper introduces the broad field of distributional semantics, the role of vector models within this field, and describes some of the results that have been made possible by the Semantic Vectors package. These applications of Semantic Vectors have so far included contributions to medical informatics and knowledge discovery, analysis of scientific articles, and even Biblical scholarship. Of particular interest is the recent emergence of models that take word order and other ordered structures into account, using permutation of coordinates to model directional relationships and semantic predicates.