Add to ORKGWe propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We apply an efficient online and distributed learning method. Experiments on various benchmark tasks—word similarity ranking, analogies, sentence completion, and sentiment analysis—demonstrate that the method outperforms or is competitive with state-of-the-art neural network representations. Our word representations are available a
This paper presents a deep learning model of building up hierarchical image represen-tation. Each la...
This paper seeks to combine dictionary learning and hierarchical image representation in a principle...
The problem with distributed representations generated by neural networks is that the meaning of the...
<p>We propose a new method for learning word representations using hierarchical regularization in sp...
Abstract. Sparse coding plays a key role in high dimensional data anal-ysis. One critical challenge ...
This paper introduces an elemental building block which combines Dictionary Learning and Dimension R...
Combined with neural language models, distributed word representations achieve significant advantage...
International audienceThis paper introduces a novel approach to learn visually grounded meaning repr...
The research topic studied in this dissertation is word representation learning, which aims to learn...
CoNLL 2015 featured a shared task on shallow discourse parsing. In 2016, the efforts continued with ...
Dictionary learning algorithms, aiming to learn a sparsifying transform from train-ing data, are oft...
Distributed word representations are widely used for modeling words in NLP tasks. Most of the existi...
Topic models with sparsity enhancement have been proven to be effective at learn- ing discriminative...
The demand for Natural Language Processing has been thriving rapidly due to the various emerging Int...
Prediction without justification has limited utility. Much of the success of neural models can be at...
This paper presents a deep learning model of building up hierarchical image represen-tation. Each la...
This paper seeks to combine dictionary learning and hierarchical image representation in a principle...
The problem with distributed representations generated by neural networks is that the meaning of the...
<p>We propose a new method for learning word representations using hierarchical regularization in sp...
Abstract. Sparse coding plays a key role in high dimensional data anal-ysis. One critical challenge ...
This paper introduces an elemental building block which combines Dictionary Learning and Dimension R...
Combined with neural language models, distributed word representations achieve significant advantage...
International audienceThis paper introduces a novel approach to learn visually grounded meaning repr...
The research topic studied in this dissertation is word representation learning, which aims to learn...
CoNLL 2015 featured a shared task on shallow discourse parsing. In 2016, the efforts continued with ...
Dictionary learning algorithms, aiming to learn a sparsifying transform from train-ing data, are oft...
Distributed word representations are widely used for modeling words in NLP tasks. Most of the existi...
Topic models with sparsity enhancement have been proven to be effective at learn- ing discriminative...
The demand for Natural Language Processing has been thriving rapidly due to the various emerging Int...
Prediction without justification has limited utility. Much of the success of neural models can be at...
This paper presents a deep learning model of building up hierarchical image represen-tation. Each la...
This paper seeks to combine dictionary learning and hierarchical image representation in a principle...
The problem with distributed representations generated by neural networks is that the meaning of the...