Text Categorization
35 papers with code • 0 benchmarks • 4 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
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Libraries
Use these libraries to find Text Categorization models and implementations
Multilingual Reuters
DAWT
Most implemented papers
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
Sarcasm detection is a key task for many natural language processing tasks.
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis
In this paper we investigate the impact of simple text preprocessing decisions (particularly tokenizing, lemmatizing, lowercasing and multiword grouping) on the performance of a standard neural text classifier.
Latent Dirichlet Allocation
Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.
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.
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.
pke: an open source python-based keyphrase extraction toolkit
We describe pke, an open source python-based keyphrase extraction toolkit.
Neural Discourse Structure for Text Categorization
We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization.
Authorship Attribution Using Text Distortion
A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre.
Discriminating between Similar Languages using Weighted Subword Features
The present contribution revolves around a contrastive subword n-gram model which has been tested in the Discriminating between Similar Languages shared task.
An Automated Text Categorization Framework based on Hyperparameter Optimization
The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution.
