An Automated Text Categorization Framework based on Hyperparameter Optimization (2017-04-06T00:00:00.000000Z)

TL;DR

A minimalistic and wide system able to tackle text classification tasks independent of domain and language, namely microTC is proposed, composed of some easy to implement text transformations, text representations, and a supervised learning algorithm that produces a competitive classifier even in the domain of informally written text.

Authors

Eric Sadit Tellez

1 papers

Daniela Moctezuma

1 papers

Sabino Miranda-Jiménez

1 papers

Mario Graff

1 papers

TL;DR

Authors

References50 items

Universal

N-GrAM: New Groningen Author-profiling Model

A Simple Approach to Multilingual Polarity Classification in Twitter

Universal, Unsupervised, Uncovered Sentiment Analysis

Multilingual Twitter Sentiment Classification: The Role of Human Annotators

Discriminative subprofile-specific representations for author profiling in social media

Recurrent Convolutional Neural Networks for Text Classification

Term-weighting learning via genetic programming for text classification

GloVe: Global Vectors for Word Representation

Distributed Representations of Words and Phrases and their Compositionality

Document classification by topic labeling

ROC analysis of classifiers in machine learning: A survey

Spam filtering using semantic similarity approach and adaptive BPNN

Random Search for Hyper-Parameter Optimization

A Review of Machine Learning Algorithms for Text-Documents Classification

Book Review: Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper

Reactive Search and Intelligent Optimization

LIBLINEAR: A Library for Large Linear Classification

Evolving local and global weighting schemes in information retrieval

Supervised term weighting for automated text categorization

Machine learning in automated text categorization

An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages

An evaluation of Naive Bayesian anti-spam filtering

CHARACTER

A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

Principal component analysis

Universal, Unsupervised (rule-based), Uncovered Sentiment Analysis

Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter

Gender and language-variety Identification with MicroTC

PAN 2017: Author Profiling - Gender and Language Variety Prediction

How Translation Alters Sentiment

Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre Evaluations

Sentiment after Translation: A Case-Study on Arabic Social Media Posts

INAOE's Participation at PAN'15: Author Profiling task

Overview of the Author Profiling Task at PAN 2013

Author Profiling: Predicting Age and Gender from Blogs Notebook for PAN at CLEF 2013

Ensemble-based Classification for Author Profiling Using Various Features Notebook for PAN at CLEF 2013

Learning to Filter Unsolicited Commercial E-Mail

Relevance Feedback

Advanced Data Mining Techniques

Improving Methods for Single-label Text Categorization

Learning to Filter Unsolicited Commercial E-Mail

A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists

An introduction to latent semantic analysis

Relevance feedback in information retrieval

Search Methodologies

G = { G i } is the set of tokenizer functions

H is a set of functions that transform a bag of tokens v into a vector (cid:126)v of dimension d , i.e., h : { S } + → R d where S is a non empty string, h ∈ H

url-handlers

punctuation-removal

Field of Study

Journal Information

Name

Volume

Venue Information

Name

Type

URL

Alternate Names