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Found 12 papers, 5 papers with code
Multimodal Weighted Fusion of Transformers for Movie Genre Classification
The Multimodal Transformer showed to be a competitive model for multimodal tasks involving textual, visual and audio signals.
Masking and Transformer-based Models for Hyperpartisanship Detection in News
Hyperpartisan news show an extreme manipulation of reality based on an underlying and extreme ideological orientation.
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
Unmasking Bias in News
We present experiments on detecting hyperpartisanship in news using a 'masking' method that allows us to assess the role of style vs. content for the task at hand.
A Comparative Analysis of Distributional Term Representations for Author Profiling in Social Media
Author Profiling (AP) aims at predicting specific characteristics from a group of authors by analyzing their written documents.
A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams
However, most standard and state-of-the-art supervised machine learning models are not well suited to deal with this scenario.
Ranked #2 on
Depression Detection
on eRisk 2017
Semantically-informed distance and similarity measures for paraphrase plagiarism identification
Paraphrase plagiarism identification represents a very complex task given that plagiarized texts are intentionally modified through several rewording techniques.
A visual approach for age and gender identification on Twitter
The goal of Author Profiling (AP) is to identify demographic aspects (e. g., age, gender) from a given set of authors by analyzing their written texts.
Gated Multimodal Units for Information Fusion
The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities.
Early text classification: a Naive solution
A document is processed as a sequence of terms, and the goal is to devise a method that can make predictions as fast as possible.
Term-Weighting Learning via Genetic Programming for Text Classification
We propose in this article a genetic program that aims at learning effective TWSs that can improve the performance of current schemes in text classification.
