Results 11 to 20 of about 14,560 (254)
A semi supervised approach to Arabic aspect category detection using Bert and teacher-student model [PDF]
PeerJ Computer Science, 2023 Aspect-based sentiment analysis tasks are well researched in English. However, we find such research lacking in the context of the Arabic language, especially with reference to aspect category detection.
Miada Almasri +2 more
doaj +3 more sources
SemEval-2016 Task 4: Sentiment Analysis in Twitter [PDF]
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016 This paper discusses the fourth year of the ``Sentiment Analysis in Twitter Task''. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new
Preslav Nakov, Fabrizio Sebastiani
exaly +6 more sources
Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007 In this paper, we present the details of the Arabic Semantic Labeling task. We describe some of the features of Arabic that are relevant for the task. The task comprises two subtasks: Arabic word sense disambiguation and Arabic semantic role labeling. The task focuses on modern standard Arabic.
Mona Diab +5 more
openalex +3 more sources
Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007 The SemEval-2007 task to disambiguate prepositions was designed as a lexical sample task. A set of over 25,000 instances was developed, covering 34 of the most frequent English prepositions, with two-thirds of the instances for training and one-third as the test set.
Ken Litkowski, Orin Hargraves
openalex +3 more sources
Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007 The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, we describe the data set used in the evaluation and the results obtained by the participating systems.
Strapparava C, Mihalcea R
exaly +4 more sources
SemEval-2018 Task 1: Affect in Tweets [PDF]
Proceedings of The 12th International Workshop on Semantic Evaluation, 2018 We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3.
Mohammad Salameh +3 more
exaly +3 more sources
Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007 This task consists of recognizing words and phrases that evoke semantic frames as defined in the FrameNet project (http://framenet.icsi.berkeley.edu), and their semantic dependents, which are usually, but not always, their syntactic dependents (including subjects). The training data was FN annotated sentences.
Collin F. Baker +2 more
openalex +4 more sources
Understanding Citizen Issues through Reviews: A Step towards Data Informed Planning in Smart Cities
Applied Sciences (Switzerland), 2018 Governments these days are demanding better Smart City technologies in order to connect with citizens and understand their demands. For such governments, much needed information exists on social media where members belonging to diverse groups share ...
Noman Dilawar +2 more
exaly +3 more sources
SemEval-2017 Task 4: Sentiment Analysis in Twitter [PDF]
Proceedings of the 11th International Workshop on Semantic Evaluation
(SemEval-2017), 2017 sentiment analysis, Twitter, classification, quantification, ranking, English ...
Noura Farra, Preslav Nakov
exaly +4 more sources
Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm [PDF]
Diagnostics, 2023 Depression is increasingly prevalent, leading to higher suicide risk. Depression detection and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust
Jaskaran Singh +4 more
doaj +2 more sources