Text classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.
( Image credit: Text Classification Algorithms: A Survey )
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Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting.
Ranked #9 on Sentiment Analysis on IMDb
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
Ranked #1 on Sentiment Analysis on SST-2 Binary classification
COMMON SENSE REASONING COREFERENCE RESOLUTION DOCUMENT SUMMARIZATION LINGUISTIC ACCEPTABILITY MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING WORD SENSE DISAMBIGUATION
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
Ranked #1 on Text Classification on IMDb
We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models.
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations.
Ranked #21 on Machine Translation on WMT2014 English-German