task ID: subtaska

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Kea: Expression-level Sentiment Analysis from Twitter Data

Authors: nan

Publication: TIPSTER (2013)

DOI: nan

This paper describes an expression-level senti- ment detection system that participated in the subtask A of SemEval-2013 Task 2: Senti- ment Analysis in Twitter. Our system uses a supervised approach …...

22994

LT3: Sentiment Classification in User-Generated Content Using a Rich Feature Set

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

This paper describes our contribution to the SemEval-2014 Task 9 on sentiment analysis in Twitter. We participated in both strands of the task, viz. classification at message-level (subtask B), and …...

22791

ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

In this paper, we describe our participation in the first shared task on automated stance de- tection (SemEval 2016 Task 6). We consider the task as a multidimensional classification problem …...

22517

PMI-cool at SemEval-2016 Task 3: Experiments with PMI and Goodness Polarity Lexicons for Community Question Answering

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

We describe our submission to SemEval-2016 Task 3 on Community Question Answering. We participated in subtask A, which asks to rerank the comments from the thread for a given forum …...

22453

SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

We describe our system for finding good an- swers in a community forum, as defined in SemEval-2016, Task 3 on Community Ques- tion Answering. Our approach relies on sev- eral …...

22455

SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

This paper describes our approach to the Se- mEval 2016 task 4, “Sentiment Analysis in Twitter”, where we participated in subtask A. Our system relies on AlchemyAPI and Senti- WordNet …...

22579

SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

We present the system we built for partici- pating in SemEval-2016 Task 3 on Commu- nity Question Answering. We achieved the best results on subtask C, and strong results on …...

22456

TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

This paper describes the participation of the team “TwiSE” in the SemEval 2016 challenge. Specifically, we participated in Task 4, namely “Sentiment Analysis in Twitter” for which we implemented sentiment …...

22576

DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractIn this paper we present two deep-learning systems that competed at SemEval-2017 Task 4 “Sentiment Analysis in Twitter”. We participated in all subtasks for English tweets, involving message-level and topic-based …...

25233

ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper describes our submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours. Given a rumoured tweet and a lot of reply tweets, the …...

25284

EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractWe describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. …...

25326

ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods

Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractThe paper describes our submissions to task 3 in SemEval-2018. There are two subtasks: Subtask A is a binary classification task to determine whether a tweet is ironic, and Subtask …...

26037

ABARUAH at SemEval-2019 Task 5 : Bi-directional LSTM for Hate Speech Detection

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractIn this paper, we present the results obtained using bi-directional long short-term memory (BiLSTM) with and without attention and Logistic Regression (LR) models for SemEval-2019 Task 5 titled ”HatEval: Multilingual …...

25808

Fermi at SemEval-2019 Task 8: An elementary but effective approach to Question Discernment in Community QA Forums

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractOnline Community Question Answering Forums (cQA) have gained massive popularity within recent years. The rise in users for such forums have led to the increase in the need for automated …...

25469

m_y at SemEval-2019 Task 9: Exploring BERT for Suggestion Mining

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractThis paper presents our system to the SemEval-2019 Task 9, Suggestion Mining from Online Reviews and Forums. The goal of this task is to extract suggestions such as the expressions …...

25650

Pardeep at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Deep Learning

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractThe rise of social media has made information exchange faster and easier among the people. However, in recent times, the use of offensive language has seen an upsurge in social …...

25687

ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractThis paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of …...

25450

TMLab SRPOL at SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractThe article describes our submission to SemEval 2019 Task 8 on Fact-Checking in Community Forums. The systems under discussion participated in Subtask A: decide whether a question asks for factual …...

25467

UVA Wahoos at SemEval-2019 Task 6: Hate Speech Identification using Ensemble Machine Learning

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractWith the growth in the usage of social media, it has become increasingly common for people to hide behind a mask and abuse others. We have attempted to detect such …...

25673

YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractThis paper describes the system submitted to SemEval 2019 Task 6: OffensEval 2019. The task aims to identify and categorize offensive language in social media, we only participate in Sub-task …...

25652

I2C at SemEval-2020 Task 12: Simple but Effective Approaches to Offensive Speech Detection in Twitter

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThis paper describes the systems developed for I2C Group to participate on Subtasks A and B in English, and Subtask A in Turkish and Arabic in OffensEval (Task 12 of …...

24405

Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-TaskLearning for Offensive Language Detection

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractNowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task. In this paper, we build an offensive language detection system, …...

24395

NLP@JUST at SemEval-2020 Task 4: Ensemble Technique for BERT and Roberta to Evaluate Commonsense Validation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThis paper presents the work of the NLP@JUST team at SemEval-2020 Task 4 competition that related to commonsense validation and explanation (ComVE) task. The team participates in sub-taskA (Validation) which …...

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TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractHate speech detection on social media platforms is crucial as it helps to avoid severe situations, and severe harm to marginalized people and groups. The application of Natural Language Processing(NLP) …...

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TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractIn this paper, we present our submission for subtask A of the Common Sense Validation and Explanation (ComVE) shared task. We examine the ability of large-scale pre-trained language models to …...

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UniTuebingenCL at SemEval-2020 Task 7: Humor Detection in News Headlines

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Publication: TIPSTER (2020)

DOI: nan

AbstractThis paper describes the work done by the team UniTuebingenCL for the SemEval 2020 Task 7: “Assessing the Funniness of Edited News Headlines”. We participated in both sub-tasks: sub-task A, …...

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YNU-oxz at SemEval-2020 Task 4: Commonsense Validation Using BERT with Bidirectional GRU

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThis paper describes the system and results of our team participated in SemEval-2020 Task4: Commonsense Validation and Explanation (ComVE), which aim to distinguish meaningful natural language statements from unreasonable natural …...

24581

daminglu123 at SemEval-2022 Task 2: Using BERT and LSTM to Do Text Classification

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractMultiword expressions (MWEs) or idiomaticity are common phenomenon in natural languages. Current pre-trained language models cannot effectively capture the meaning of these MWEs. The reason is that two normal words, …...

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DD-TIG at SemEval-2022 Task 5: Investigating the Relationships Between Multimodal and Unimodal Information in Misogynous Memes Detection and Classification

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis paper describes our submission for task 5 Multimedia Automatic Misogyny Identification (MAMI) at SemEval-2022. The task is designed to detect and classify misogynous memes. To utilize both textual and …...

25096

DUCS at SemEval-2022 Task 6: Exploring Emojis and Sentiments for Sarcasm Detection

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis paper describes the participation of team DUCS at SemEval 2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and Arabic. Team DUCS participated in SubTask A of iSarcasmEval …...

25035

I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractIn this paper we present our approach and system description on iSarcasmEval: a SemEval task for intended sarcasm detection on social networks. This derives from our participation in SubTask A: …...

25057

R2D2 at SemEval-2022 Task 5: Attention is only as good as its Values! A multimodal system for identifying misogynist memes

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis paper describes the multimodal deep learning system proposed for SemEval 2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. We participated in both Subtasks, i.e. Subtask A: Misogynous meme …...

25067

SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractIn this paper, we describe our efforts at SemEval 2022 Shared Task 4 on Patronizing and Condescending Language (PCL) Detection. This is the first shared task to detect PCL which …...

25120

X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis paper describes our winning system on SemEval 2022 Task 7: Identifying Plausible Clarifications ofImplicit and Underspecified Phrases in Instructional Texts. A replaced token detection pre-trained model is utilized with …...

25025

ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis paper presents our contribution to the SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding.We explore the impact of three different pre-trained multilingual language models in the SubTaskA.By enhancing …...

25148

HHS at SemEval-2023 Task 10: A Comparative Analysis of Sexism Detection Based on the RoBERTa Model

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractThis paper describes the methods and models applied by our team HHS in SubTask-A of SemEval-2023 Task 10 about sexism detection. In this task, we trained with the officially released …...

24843

hhuEDOS at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) Binary Sexism Detection (Subtask A)

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractIn this paper, we describe SemEval-2023 Task 10, a shared task on detecting and predicting sexist language. The dataset consists of labeled sexist and non-sexist data targeted towards women acquired …...

24773

TüReuth Legal at SemEval-2023 Task 6: Modelling Local and Global Structure of Judgements for Rhetorical Role Prediction

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractThis paper describes our system for SemEval-2023 Task 6: LegalEval: Understanding Legal Texts. We only participate in Sub-Task (A), Predicting Rhetorical Roles. Our final submission achieves 73.35 test set F1 …...

24933