SemEval-2016 Task 5: Aspect Based Sentiment Analysis [PDF]
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016 International audienceThis paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015.
S Senthilvelan +2 more
exaly +11 more sources
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
Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge [PDF]
Proceedings of The 12th International Workshop on Semantic
Evaluation (2018), p. 985-989, 2018 Luminoso participated in the SemEval 2018 task on "Capturing Discriminative Attributes" with a system based on ConceptNet, an open knowledge graph focused on general knowledge. In this paper, we describe how we trained a linear classifier on a small number of semantically-informed features to achieve an $F_1$ score of 0.7368 on the task, close to the ...
Lowry-Duda, Joanna, Speer, Robyn
arxiv +3 more sources
Duluth at SemEval-2017 Task 6: Language Models in Humor Detection [PDF]
arXiv, 2017 This paper describes the Duluth system that participated in SemEval-2017 Task 6 #HashtagWars: Learning a Sense of Humor. The system participated in Subtasks A and B using N-gram language models, ranking highly in the task evaluation. This paper discusses the results of our system in the development and evaluation stages and from two post-evaluation ...
Pedersen, Ted, Yan, Xinru
arxiv +3 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
OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers using Piecewise Convolutional Neural Networks [PDF]
arXiv, 2018 We describe our system for SemEval-2018 Shared Task on Semantic Relation Extraction and Classification in Scientific Papers where we focus on the Classification task. Our simple piecewise convolution neural encoder performs decently in an end to end manner. A simple inter-task data augmentation signifi- cantly boosts the performance of the model.
Dhyani, Dushyanta
arxiv +3 more sources
Understanding Citizen Issues through Reviews: A Step towards Data Informed Planning in Smart Cities
Applied Sciences, 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 +6 more
doaj +3 more sources
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications [PDF]
, 2017 We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials.
Augenstein, Isabelle +4 more
exaly +3 more sources
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs [PDF]
arXiv, 2017 In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings.
arxiv +2 more sources
Multilingual Fine-Grained Named Entity Recognition [PDF]
Computer Science Journal of Moldova, 2023 The “MultiCoNER II Multilingual Complex Named Entity Recognition” task\footnote[1]{\url{https://multiconer.github.io}} within SemEval 2023 competition focuses on identifying complex named entities (NEs), such as the titles of creative works (e.g., songs,
Viorica-Camelia Lupancu, Adrian Iftene
doaj +1 more source