Towards Wide Learning: Experiments in Healthcare

sayakpaul/Generating-categories-from-arXiv-paper-titles • 17 Dec 2016

In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline.

Transfer Learning for Improving Speech Emotion Classification Accuracy

raulsteleac/Speech_Emotion_Recognition • • 19 Jan 2018

The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same conditions.

Pop Music Highlighter: Marking the Emotion Keypoints

remyhuang/pop-music-highlighter • • 28 Feb 2018

In a previous work, we introduced an attention-based convolutional recurrent neural network that uses music emotion classification as a surrogate task for music highlight extraction, for Pop songs.

Inducing a Lexicon of Abusive Words – a Feature-Based Approach

miwieg/naacl2018 • HLT 2018

IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations

jabalazs/implicit_emotion • • WS 2018

In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task (IEST), which obtained 2$^{\text{nd}}$ place out of 26 teams with a test macro F1 score of $0. 710$.

NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification

alexandra-chron/wassa-2018 • • WS 2018

In this paper we present our approach to tackle the Implicit Emotion Shared Task (IEST) organized as part of WASSA 2018 at EMNLP 2018.

Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition

zeroQiaoba/EmotiW2018 • • 13 Sep 2018

We test our method in the EmotiW 2018 challenge and we gain promising results.

DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning

ysenarath/opinion-lab • WS 2018

This paper describes an approach to solve implicit emotion classification with the use of pre-trained word embedding models to train multiple neural networks.

BrainT at IEST 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion Classification

ims-teamlab2018/Braint • WS 2018

We present \textit{BrainT}, a multi-class, averaged perceptron tested on implicit emotion prediction of tweets.

Practical Text Classification With Large Pre-Trained Language Models

NVIDIA/sentiment-discovery • • 4 Dec 2018

Multi-emotion sentiment classification is a natural language processing (NLP) problem with valuable use cases on real-world data.