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Text Classification

185 papers with code ยท 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.

( Image credit: Text Classification Algorithms: A Survey )

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Latest papers without code

Building Hierarchical Interpretations in Natural Language via Feature Interaction Detection

ICLR 2020

Existing explanation generation methods usually provide important features by scoring their individual contributions to the model prediction and ignore the interactions between features, which eventually provide a bag-of-words representation as explanation.

TEXT CLASSIFICATION

Generalized Zero-shot ICD Coding

ICLR 2020

The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses.

MULTI-LABEL TEXT CLASSIFICATION TEXT CLASSIFICATION ZERO-SHOT LEARNING

BERT for Sequence-to-Sequence Milti-Label Text Classification

ICLR 2020

We study the BERT language representation model and the sequence generation model with BERT encoder for multi-label text classification task.

MULTI-LABEL CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION TEXT CLASSIFICATION

Incorporating BERT into Neural Machine Translation

ICLR 2020

The recently proposed BERT~\citep{devlin2018bert} has shown great power on a variety of natural language understanding tasks, such as text classification, reading comprehension, etc.

READING COMPREHENSION TEXT CLASSIFICATION UNSUPERVISED MACHINE TRANSLATION

Anchor & Transform: Learning Sparse Representations of Discrete Objects

ICLR 2020

Learning continuous representations of discrete objects such as text, users, and items lies at the heart of many applications including text and user modeling.

LANGUAGE MODELLING TEXT CLASSIFICATION

EDUCE: Explaining model Decision through Unsupervised Concepts Extraction

ICLR 2020

Therefore, we propose a new self-interpretable model that performs output prediction and simultaneously provides an explanation in terms of the presence of particular concepts in the input.

SENTIMENT ANALYSIS TEXT CLASSIFICATION

Unsupervised Data Augmentation for Consistency Training

ICLR 2020

In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.

DATA AUGMENTATION TEXT CLASSIFICATION TRANSFER LEARNING

Soft Token Matching for Interpretable Low-Resource Classification

ICLR 2020

We propose a model to tackle classification tasks in the presence of very little training data.

TEXT CLASSIFICATION

Federated Adversarial Domain Adaptation

ICLR 2020

In this work, we present a principled approach to the problem of federated domain adaptation, which aims to align the representations learned among the different nodes with the data distribution of the target node.

DOMAIN ADAPTATION TEXT CLASSIFICATION TRANSFER LEARNING

Encoding word order in complex embeddings

ICLR 2020

Our solution generalizes word embeddings, previously defined as independent vectors, to continuous word functions over a variable (position).

LANGUAGE MODELLING MACHINE TRANSLATION TEXT CLASSIFICATION WORD EMBEDDINGS