Text Categorization
34 papers with code • 0 benchmarks • 3 datasets
Text Categorization is the task of automatically assigning pre-defined categories to documents written in natural languages. Several types of Text Categorization have been studied, each of which deals with different types of documents and categories, such as topic categorization to detect discussed topics (e.g., sports, politics), spam detection, and sentiment classification to determine the sentiment typically in product or movie reviews.
Source: Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
Benchmarks
Multilingual Reuters
DAWT
Greatest papers with code
Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers
juand-r/entity-recognition-datasets
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Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC) dataset is a collection of automatically categorized and annotated sentences obtained from Wikipedia.
pke: an open source python-based keyphrase extraction toolkit
We describe pke, an open source python-based keyphrase extraction toolkit.
PySS3: A Python package implementing a novel text classifier with visualization tools for Explainable AI
A recently introduced text classifier, called SS3, has obtained state-of-the-art performance on the CLEF's eRisk tasks.
t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams
SS3 was created to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale.
Ranked #1 on
Depression Detection
on eRisk 2017
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
Term weighting schemes often dominate the performance of many classifiers, such as kNN, centroid-based classifier and SVMs.
Ranked #1 on
Multi-class Classification
on Reuters-52
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization
The lower the HS level, the less the categorization performance.
Massively Multilingual Word Embeddings
We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space.
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications
cair/TextUnderstandingTsetlinMachine
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The Tsetlin Machine either performs on par with or outperforms all of the evaluated methods on both the 20 Newsgroups and IMDb datasets, as well as on a non-public clinical dataset.
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
HongyangGao/hConv-gPool-Net
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Another limitation of GCN when used on graph-based text representation tasks is that, GCNs do not consider the order information of nodes in graph.
Structure-Aware Convolutional Neural Networks
vector-1127/SACNNs
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NeurIPS 2018
Convolutional neural networks (CNNs) are inherently subject to invariable filters that can only aggregate local inputs with the same topological structures.
