Brainy: A Machine Learning Library

Konkol, Michal

doi:10.1007/978-3-319-07176-3_43

Cited by 27 publications

(17 citation statements)

References 4 publications

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“…We use Maximum Entropy classifier from Brainy machine learning library (Konkol, 2014) and UD-Pipe (Straka et al, 2016) for preprocessing and doesn't use any lexicons, just word embeddings. The system is based on (Hercig et al, 2016).…”

Section: Brainy Systemmentioning

confidence: 99%

UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

Přibáň

Hercig

Lenc

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English, Arabic, and Spanish.For the regression subtask we use the AffectiveTweets system with added features using various word embeddings, lexicons, and LDA. For the ordinal classification we additionally use our Brainy system with features using parse tree, POS tags, and morphological features. The most beneficial features apart from word and character n-grams include word embeddings, POS count and morphological features.

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Section: Brainy Systemmentioning

confidence: 99%

UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

Přibáň

Hercig

Lenc

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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“…For all experiments we use Maximum Entropy classifier with default settings from Brainy machine learning library (Konkol, 2014 cases lemmatization 2 . We utilize morphological analysis, parse trees, lemmatization, and POS tags from UDPipe (Straka et al, 2016).…”

Section: System Descriptionmentioning

confidence: 99%

UWB at SemEval-2018 Task 3: Irony detection in English tweets

Hercig

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes our system created for the SemEval-2018 Task 3: Irony detection in English tweets. Our strongly constrained system uses only the provided training data without any additional external resources. Our system is based on Maximum Entropy classifier and various features using parse tree, POS tags, and morphological features. Even without additional lexicons and word embeddings we achieved fourth place in Subtask A and seventh in Subtask B in terms of accuracy.

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“…We train a supervised system based upon the Maximum Entropy classifier using the Brainy tool (Konkol, 2014). We use separate models for verb and non-verb predicates.…”

Section: Classifier and Featuresmentioning

confidence: 99%

Cross-Lingual SRL Based upon Universal Dependencies

Pražák

Konopík

2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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In this paper, we introduce a cross-lingual Semantic Role Labeling (SRL) system with language independent features based upon Universal Dependencies. We propose two methods to convert SRL annotations from monolingual dependency trees into universal dependency trees. Our SRL system is based upon cross-lingual features derived from universal dependency trees and supervised learning that utilizes a maximum entropy classifier. We design experiments to verify whether the Universal Dependencies are suitable for the cross-lingual SRL. The results are very promising and they open new interesting research paths for the future.

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Brainy: A Machine Learning Library

Cited by 27 publications

References 4 publications

UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

UWB at SemEval-2018 Task 3: Irony detection in English tweets

Cross-Lingual SRL Based upon Universal Dependencies

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