In this paper, we propose two different systems for classifying offensive language in micro-blog messages from Twitter (”tweet”). The first system uses an ensemble of convolutional neural networks (CNN), whose outputs are then fed to a meta-classifier for the final prediction. The second system uses a combination of a CNN and a gated recurrent unit (GRU) together with a transfer-learning approach based on pretraining with a large, automatically translated dataset
Today international on-line content material has turned out to be a first-rate part due to growth in...
Sentiment analysis of short social media texts is a challenging task due to limited contextual infor...
We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offen-sive l...
In this paper, we propose two different systems for classifying offensive language in micro-blog mes...
Social media platforms receive massive amounts of user-generated content that may include offensive ...
In this paper, we describe two systems for predicting message-level offensive language in German twe...
In this paper, we describe two systems for predicting message-level offensive language in German twe...
This paper describes the entry hshl coarse 1.txt for Task I (Binary Classification) of the Germeval ...
This paper reports on the systems the RuG Team submitted to the GermEval 2018 - Shared Task on the I...
We investigate different strategies for automatic offensive language classification on German Twitte...
In this paper we describe our submissions to task I of the GermEval 2018 Shared Task with the goal o...
Social media has been an effective carrier of information from the day of its inception. People worl...
This paper describes the system submitted by the RGCL team to GermEval 2019 Shared Task 2: Identific...
The paper describes the systems submitted to OffensEval (SemEval 2019, Task 6) on ‘Identifying and C...
This paper reports on the systems the RuG Team submitted to the GermEval 2018 - Shared Task on the I...
Today international on-line content material has turned out to be a first-rate part due to growth in...
Sentiment analysis of short social media texts is a challenging task due to limited contextual infor...
We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offen-sive l...
In this paper, we propose two different systems for classifying offensive language in micro-blog mes...
Social media platforms receive massive amounts of user-generated content that may include offensive ...
In this paper, we describe two systems for predicting message-level offensive language in German twe...
In this paper, we describe two systems for predicting message-level offensive language in German twe...
This paper describes the entry hshl coarse 1.txt for Task I (Binary Classification) of the Germeval ...
This paper reports on the systems the RuG Team submitted to the GermEval 2018 - Shared Task on the I...
We investigate different strategies for automatic offensive language classification on German Twitte...
In this paper we describe our submissions to task I of the GermEval 2018 Shared Task with the goal o...
Social media has been an effective carrier of information from the day of its inception. People worl...
This paper describes the system submitted by the RGCL team to GermEval 2019 Shared Task 2: Identific...
The paper describes the systems submitted to OffensEval (SemEval 2019, Task 6) on ‘Identifying and C...
This paper reports on the systems the RuG Team submitted to the GermEval 2018 - Shared Task on the I...
Today international on-line content material has turned out to be a first-rate part due to growth in...
Sentiment analysis of short social media texts is a challenging task due to limited contextual infor...
We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offen-sive l...