
Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons
We propose postprocessing method for enriching not only word representa...
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Neural Vector Conceptualization for Word Vector Space Interpretation
Distributed word vector spaces are considered hard to interpret which hi...
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Evaluating vectorspace models of analogy
Vectorspace representations provide geometric tools for reasoning about...
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Analyzing Structures in the Semantic Vector Space: A Framework for Decomposing Word Embeddings
Word embeddings are rich word representations, which in combination with...
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Build2Vec: Building Representation in Vector Space
In this paper, we represent a methodology of a graph embeddings algorith...
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A Visual Embedding for the Unsupervised Extraction of Abstract Semantics
Vectorspace word representations obtained from neural network models ha...
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Key Phrase Extraction Applause Prediction
With the increase in content availability over the internet it is very d...
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Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings
Selecting a representative vector for a set of vectors is a very common requirement in many algorithmic tasks. Traditionally, the mean or median vector is selected. Ontology classes are sets of homogeneous instance objects that can be converted to a vector space by word vector embeddings. This study proposes a methodology to derive a representative vector for ontology classes whose instances were converted to the vector space. We start by deriving five candidate vectors which are then used to train a machine learning model that would calculate a representative vector for the class. We show that our methodology outperforms the traditional mean and median vector representations.
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