NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis
This paper describes our multi-view ensemble approach to SemEval-2017 Task 4 on Sentiment Analysis in Twitter, specifically, the Message Polarity Classification subtask for English (subtask A). Our system is a voting ensemble, where each base classifier is trained in a different feature space. The first space is a bag-of-words model and has a Linear SVM as base classifier. The second and third spaces are two different strategies of combining word embeddings to represent sentences and use a Linear SVM and a Logistic Regressor as base classifiers. The proposed system was ranked 18th out of 38 systems considering F1 score and 20th considering recall.
Code (1)
Tasks
General ClassificationSentiment AnalysisTwitter Sentiment AnalysisWord EmbeddingsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Deep Learning Brasil -- NLP at SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets
In this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models (MultiFiT, BERT, A…
Sentiment AnalysisLijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forums
In this paper, we describe a suggestion mining system that participated in SemEval 2019 Task 9, SubTask A - Suggestion Mining from Online Reviews and Forums. Given some suggestions from online reviews and forums that can…
Suggestion miningNILC-SWORNEMO at the Surface Realization Shared Task: Exploring Syntax-Based Word Ordering using Neural Models
This paper describes the submission by the NILC Computational Linguistics research group of the University of S{\~a}o Paulo/Brazil to the Track 1 of the Surface Realization Shared Task (SRST Track 1). We present a neural…
Language ModelingLanguage ModellingLEMMASentence+1NILC: Discovering New Intents with LLM-assisted Clustering
New intent discovery (NID) seeks to recognize both new and known intents from unlabeled user utterances, which finds prevalent use in practical dialogue systems. Existing works towards NID mainly adopt a cascaded archite…
Intent DiscoveryNL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining
In this paper, we present neural model architecture submitted to the SemEval-2019 Task 9 competition: "Suggestion Mining from Online Reviews and Forums". We participated in both subtasks for domain specific and also cros…
Suggestion mining