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Papers by Alina Lorent
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
This paper presents Wild Devs' participation in the SemEval-2017 Task 2 "Multilingual and Cross-l... more This paper presents Wild Devs' participation in the SemEval-2017 Task 2 "Multilingual and Cross-lingual Semantic Word Similarity", which tries to automatically measure the semantic similarity between two words. The system was build using neural networks, having as input a collection of word pairs, whereas the output consists of a list of scores, from 0 to 4, corresponding to the degree of similarity between the word pairs.
Proceedings of The 12th International Workshop on Semantic Evaluation
The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent e... more The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network).
Proceedings of The 12th International Workshop on Semantic Evaluation
The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent e... more The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network).
Proceedings of The 12th International Workshop on Semantic Evaluation
The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent e... more The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network).
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
This paper presents Wild Devs' participation in the SemEval-2017 Task 2 "Multilingual and Cross-l... more This paper presents Wild Devs' participation in the SemEval-2017 Task 2 "Multilingual and Cross-lingual Semantic Word Similarity", which tries to automatically measure the semantic similarity between two words. The system was build using neural networks, having as input a collection of word pairs, whereas the output consists of a list of scores, from 0 to 4, corresponding to the degree of similarity between the word pairs.
Proceedings of The 12th International Workshop on Semantic Evaluation
The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent e... more The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network).
Proceedings of The 12th International Workshop on Semantic Evaluation
The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent e... more The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network).
Proceedings of The 12th International Workshop on Semantic Evaluation
The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent e... more The "Multilingual Emoji Prediction" task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network).