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Text Classification

151 papers with code · Natural Language Processing

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.

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Latest papers with code

Message Passing Attention Networks for Document Understanding

17 Aug 2019giannisnik/mpad

In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD).

TEXT CLASSIFICATION

4
17 Aug 2019

Few-shot Text Classification with Distributional Signatures

16 Aug 2019YujiaBao/Distributional-Signatures

In this paper, we explore meta-learning for few-shot text classification.

META-LEARNING RELATION CLASSIFICATION TEXT CLASSIFICATION

14
16 Aug 2019

hULMonA: The Universal Language Model in Arabic

WS 2019 aub-mind/hULMonA

Experiment results show that the developed hULMonA and multi-lingual ULM are able to generalize well to multiple Arabic data sets and achieve new state of the art results in Arabic Sentiment Analysis for some of the tested sets.

ARABIC SENTIMENT ANALYSIS LANGUAGE MODELLING TEXT CLASSIFICATION TRANSFER LEARNING

6
01 Aug 2019

Improving short text classification through global augmentation methods

7 Jul 2019dsfsi/textaugment

We study the effect of different approaches to text augmentation.

TEXT CLASSIFICATION

4
07 Jul 2019

Depth Growing for Neural Machine Translation

ACL 2019 apeterswu/Depth_Growing_NMT

While very deep neural networks have shown effectiveness for computer vision and text classification applications, how to increase the network depth of neural machine translation (NMT) models for better translation quality remains a challenging problem.

MACHINE TRANSLATION TEXT CLASSIFICATION

17
03 Jul 2019

Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency

ACL 2019 JHL-HUST/PWWS

Experiments on three popular datasets using convolutional as well as LSTM models show that PWWS reduces the classification accuracy to the most extent, and keeps a very low word substitution rate.

ADVERSARIAL ATTACK IMAGE CLASSIFICATION SEMANTIC TEXTUAL SIMILARITY TEXT CLASSIFICATION

3
01 Jul 2019

Graph Star Net for Generalized Multi-Task Learning

21 Jun 2019graph-star-team/graph_star

In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, graph classification and link prediction.

GRAPH CLASSIFICATION LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION SENTIMENT ANALYSIS TEXT CLASSIFICATION

31
21 Jun 2019