CodeForTheChange at SemEval-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering
The strengths of the scalable gradient tree boosting algorithm, XGBoost and distributed sentence encoder, Skip-Thought Vectors are not explored yet by the cQA research community. We tried to apply and combine these two effective methods for finding factual nature of the questions and answers. The work also include experimentation with other popular classifier models like AdaBoost Classifier, DecisionTree Classifier, RandomForest Classifier, ExtraTrees Classifier, XGBoost Classifier and Multi-layer Neural Network. In this paper, we present the features used, approaches followed for feature engineering, models experimented with and finally the results.
Code (0)
등록된 구현이 없습니다.
Tasks
Community Question AnsweringFact CheckingFeature EngineeringQuestion AnsweringSentenceSimilar Papers 제목 키워드 기반
TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors
This paper describes our system for the SemEval-2018 Task 12: Argument Reasoning Comprehension Task. We utilize skip-thought vectors, sentence-level distributional vectors inspired by the popular word embeddings and the …
Common Sense ReasoningNatural Language InferenceSentenceWord EmbeddingsTemporalTeller at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing
This paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical Semantic Change Detection. We develop a unified framework for the common semantic change detection pipelines including preprocessing…
Change DetectionLearning Word EmbeddingsWord EmbeddingsHope at SemEval-2019 Task 6: Mining social media language to discover offensive language
User{'}s content share through social media has reached huge proportions nowadays. However, along with the free expression of thoughts on social media, people risk getting exposed to various aggressive statements. In thi…
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder
In order to learn universal sentence representations, previous methods focus on complex recurrent neural networks or supervised learning. In this paper, we propose a mean-max attention autoencoder (mean-max AAE) within t…
DecoderSentenceIUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text using Deep Neural Networks and Linear Baselines
Sentiment Analysis is a well-studied field of Natural Language Processing. However, the rapid growth of social media and noisy content within them poses significant challenges in addressing this problem with well-establi…
Sentiment Analysis