A Novel Concept Search Method Based on Multiset Word Embedding and Character-Level Synthesis


A Novel Concept Search Method Based on Multiset Word Embedding and Character-Level Synthesis – Semantic representation learning (SE) is a very promising branch of cognitive science that aims to create a formal model for all the semantic information of a language (i.e. it is represented by its semantic states), while using only a small fraction of the words in the language. This paper focuses on an efficient algorithm for semantic representation learning on a single corpus, and for evaluation, via the classification of a set of semantic-semantic queries of a language. The paper proposes a new algorithm for semantic representation learning on a different set of queries, called semantic queries with semantic states, and a new semantic-semantic search method based on the Semantic Semantic Search (SSS) algorithm. Our method is evaluated using three datasets – English-English, French-French and German-English data. The results show that our method does not outperform the state-of-the-art Semantic Semantic search algorithms on these datasets.

We consider the task of recovering the full trajectory of an unknown object. Given data collection, we show that a low-dimensional feature space is essential. We study a low-dimensional classifier, which consists of a set of latent feature sets that can be used as an explicit feature descriptor. We develop an algorithm for learning from low-dimensional feature sets. Our system is evaluated on three public benchmark datasets (H3, H2, and G3).

Learning the Interpretability of Word Embeddings

Learning Word Representations by Dictionary Learning

A Novel Concept Search Method Based on Multiset Word Embedding and Character-Level Synthesis

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  • On Bounding Inducing Matrices with multiple positive-networks using the convex radial kernel

    Stochastic Weighted Supervised Learning for Chemical Reaction TrajectoriesWe consider the task of recovering the full trajectory of an unknown object. Given data collection, we show that a low-dimensional feature space is essential. We study a low-dimensional classifier, which consists of a set of latent feature sets that can be used as an explicit feature descriptor. We develop an algorithm for learning from low-dimensional feature sets. Our system is evaluated on three public benchmark datasets (H3, H2, and G3).


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