task ID: subtaskb

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Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

We present two supervised sentiment de- tection systems which were used to com- pete in SemEval-2014 Task 9: Senti- ment Analysis in Twitter. The first sys- tem (Rosenthal and McKeown, …...

22865

DCU: Aspect-based Polarity Classification for SemEval Task 4

Authors: nan

Publication: TIPSTER (2014)

DOI: nan

We describe the work carried out by DCU on the Aspect Based Sentiment Analysis task at SemEval 2014. Our team submit- ted one constrained run for the restaurant domain and …...

22859

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

Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the sub- task B. Our system comprises of multiple in- dependent models …...

22563

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

UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

In this work we describe the system built for the three English subtasks of the Se- mEval 2016 Task 3 by the Department of Computer Science of the University of …...

22464

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

EELECTION at SemEval-2017 Task 10: Ensemble of nEural Learners for kEyphrase ClassificaTION

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper describes our approach to the SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications, specifically to Subtask (B): Classification of identified keyphrases. We explored three different …...

25179

IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural …...

25294

SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper describes the SimBow system submitted at SemEval2017-Task3, for the question-question similarity subtask B. The proposed approach is a supervised combination of different unsupervised textual similarities. These textual similarities …...

25321

SINAI at SemEval-2017 Task 4: User based classification

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis document describes our participation in SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have only reported results for subtask B - English, determining the polarity towards a topic on …...

25249

UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThe majority of core techniques to solve many problems in Community Question Answering (CQA) task rely on similarity computation. This work focuses on similarity between two sentences (or questions in …...

25313

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

Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractThis article describes Amobee’s participation in “HatEval: Multilingual detection of hate speech against immigrants and women in Twitter” (task 5) and “OffensEval: Identifying and Categorizing Offensive Language in Social Media” …...

25806

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

MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractIn this paper we present our approach and the system description for Sub Task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in …...

25692

Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter at SemEval-2019 Task 5: Frequency Analysis Interpolation for Hate in Speech Detection

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractThis document describes a text change of representation approach to the task of Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter, as part of SemEval-2019 1 . …...

25772

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

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

GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractOffensive language detection is an important and challenging task in natural language processing. We present our submissions to the OffensEval 2020 shared task, which includes three English sub-tasks: identifying the …...

24461

JBNU at SemEval-2020 Task 4: BERT and UniLM for Commonsense Validation and Explanation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThis paper presents our contributions to the SemEval-2020 Task 4 Commonsense Validation and Explanation (ComVE) and includes the experimental results of the two Subtasks B and C of the SemEval-2020 …...

24594

Masked Reasoner at SemEval-2020 Task 4: Fine-Tuning RoBERTa for Commonsense Reasoning

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThis paper describes the masked reasoner system that participated in SemEval-2020 Task 4: Commonsense Validation and Explanation. The system participated in the subtask B.We proposes a novel method to fine-tune …...

24610

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

Authors: nan

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

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

I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractSarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the …...

25055

niksss at SemEval-2022 Task 6: Are Traditionally Pre-Trained Contextual Embeddings Enough for Detecting Intended Sarcasm ?

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis paper presents the 10th and 11th place system for Subtask A -English and Subtask A Arabic respectively of the SemEval 2022 -Task 6. The purpose of the Subtask A …...

25047

PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis paper describes the method we utilized in the SemEval-2022 Task 6 iSarcasmEval: Intended Sarcasm Detection In English and Arabic. Our system has achieved 1st in SubtaskB, which is to …...

25061

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

R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis paper describes our system used for SemEval 2022 Task 6: iSarcasmEval: Intended Sarcasm Detection in English and Arabic. We participated in all subtasks based on only English datasets. Pre-trained …...

25031

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

HITSZQ at SemEval-2023 Task 10: Category-aware Sexism Detection Model with Self-training Strategy

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractThis paper describes our system used in the SemEval-2023 \textit{Task 10 Explainable Detection of Online Sexism (EDOS)}. Specifically, we participated in subtask B: a 4-class sexism classification task, and subtask …...

24839

JUAGE at SemEval-2023 Task 10: Parameter Efficient Classification

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractUsing pre-trained language models to implement classifiers from small to modest amounts of training data is an area of active research. The ability of large language models to generalize from …...

24805