99 papers with code • 6 benchmarks • 14 datasets
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.
A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification
Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014).
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
First, the majority of datasets for sequential short-text classification (i. e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task.
Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing.
Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually.
Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution
Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision.