text-classification
897 papers with code • 0 benchmarks • 0 datasets
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Libraries
Use these libraries to find text-classification models and implementationsMost implemented papers
HotFlip: White-Box Adversarial Examples for Text Classification
makcedward/nlpaug
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ACL 2018
We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier.
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios.
Joint Embedding of Words and Labels for Text Classification
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences.
Textual Membership Queries
It uses a small amount of labeled data as the core set for the synthesis of useful membership queries (MQs) - unlabeled instances generated by an algorithm for human labeling.
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines
Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances.
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
We stochastically replace words with other words that are predicted by a bi-directional language model at the word positions.
Diverse Few-Shot Text Classification with Multiple Metrics
Gorov/DiverseFewShot_Amazon
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NAACL 2018
We study few-shot learning in natural language domains.
Information Aggregation via Dynamic Routing for Sequence Encoding
FudanNLP/Capsule4TextClassification
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COLING 2018
The dynamic routing policy is dynamically deciding that what and how much information need be transferred from each word to the final encoding of the text sequence.
Representation Learning of Entities and Documents from Knowledge Base Descriptions
In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB).
Learning Representations for Soft Skill Matching
The disambiguation is formulated as a binary text classification problem where the prediction is made for the potential soft skill based on the context where it occurs.
