The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distan...
Over the past decade, there have been promising developments in Natural Language Processing (NLP) wi...
This paper presents a deep learning architecture for the semantic decoder component of a Statistical...
Since Hinton and Salakhutdinov published their landmark science paper in 2006 ending the previous ne...
The ability to accurately represent sentences is central to language understanding. We describe a co...
The ability to accurately represent sen-tences is central to language understand-ing. We describe a ...
This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence mo...
Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vis...
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-...
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-...
The compositionality of meaning extends beyond the single sentence. Just as words combine to form th...
Deep Learning Architectures have been achieving state-of-the-art results in many application scenari...
Subjectivity detection aims to distinguish natural language as either opinionated (positive or negat...
In the Baoule language, several sentences express the same fact. Classification of sentences is a ta...
Humans are able to recognize a grammatically correct but semantically anomalous sentence. On the ta...
Convolutional Neural Networks (CNNs) and pre-trained word embeddings have revolutionized the field o...
Over the past decade, there have been promising developments in Natural Language Processing (NLP) wi...
This paper presents a deep learning architecture for the semantic decoder component of a Statistical...
Since Hinton and Salakhutdinov published their landmark science paper in 2006 ending the previous ne...
The ability to accurately represent sentences is central to language understanding. We describe a co...
The ability to accurately represent sen-tences is central to language understand-ing. We describe a ...
This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence mo...
Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vis...
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-...
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-...
The compositionality of meaning extends beyond the single sentence. Just as words combine to form th...
Deep Learning Architectures have been achieving state-of-the-art results in many application scenari...
Subjectivity detection aims to distinguish natural language as either opinionated (positive or negat...
In the Baoule language, several sentences express the same fact. Classification of sentences is a ta...
Humans are able to recognize a grammatically correct but semantically anomalous sentence. On the ta...
Convolutional Neural Networks (CNNs) and pre-trained word embeddings have revolutionized the field o...
Over the past decade, there have been promising developments in Natural Language Processing (NLP) wi...
This paper presents a deep learning architecture for the semantic decoder component of a Statistical...
Since Hinton and Salakhutdinov published their landmark science paper in 2006 ending the previous ne...
