SEMEVAL 2017

ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge

SEMEVAL 2017 commonsense/conceptnet-numberbatch

This paper describes Luminoso's participation in SemEval 2017 Task 2, "Multilingual and Cross-lingual Semantic Word Similarity", with a system based on ConceptNet.

MULTILINGUAL WORD EMBEDDINGS

Emotion Intensities in Tweets

SEMEVAL 2017 felipebravom/AffectiveTweets

This paper examines the task of detecting intensity of emotion from text.

EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION

SEMEVAL 2017 UKPLab/semeval2017-scienceie

From these approaches, we created an ensemble of differently hyper-parameterized systems, achieving a micro-F1-score of 0. 63 on the test data.

BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs

SEMEVAL 2017 mihirahlawat/Sentiment-Analysis

In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks.

TWITTER SENTIMENT ANALYSIS WORD EMBEDDINGS

FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings

SEMEVAL 2017 saleiro/Financial-Sentiment-Analysis

This paper presents the approach developed at the Faculty of Engineering of University of Porto, to participate in SemEval 2017, Task 5: Fine-grained Sentiment Analysis on Financial Microblogs and News.

SENTIMENT ANALYSIS WORD EMBEDDINGS

SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

SEMEVAL 2017 LIAAD/KeywordExtractor-Datasets

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials.

KNOWLEDGE BASE POPULATION

UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation

SEMEVAL 2017 v1n337/semeval2017-task5

The system uses text vectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled with regression model variants to predict the sentiment scores.

DATA AUGMENTATION SENTIMENT ANALYSIS