Our work investigates the linguistic structure in raw text, and explores clustering and search tasks using Word2Vec to train the underlying word representations ...

This work proposes a formulation that combines a word vector set of variable cardinality to represent a verse or a sentence, with an iterative distance metric.

Mar 27, 2017 · A Hierarchical Book Representation of Word Embeddings for Effective Semantic Clustering and Search. Avi Bleiweiss 1. Show full list: 1 author.

A Hierarchical Book Representation of Word Embeddings for Effective Semantic Clustering and Search. Avi Bleiweiss. 2017. Abstract. Semantic word embeddings ...

A Hierarchical Book Representation of Word Embeddings for Effective Semantic Clustering and Search. A. Bleiweiss. 2017, International Conference on Agents and ...

In this paper, we propose a hierarchical embedding model to learn semantic representations for entities (i.e. words, products, users and queries) from different ...

These word representations are also the first example in this book of repre- sentation learning, automatically learning useful representations of the input text ...

Jan 22, 2016 · We propose a unified framework to expand short texts based on word embedding clustering and convolutional neural network (CNN).

Feb 23, 2024 · Embeddings are numerical representations of real-world objects that machine learning (ML) and artificial intelligence (AI) systems use to ...

Dec 28, 2021 · This paper presents a method that uses prior knowledge of the application domain to support machine learning in cases with insufficient data.