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Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction
This paper describes our participation in SemEval-2016 Task 5 for Subtask 1, Slot 2. The challenge demands to find domain spe- cific target expressions on sentence level that refer to …...
UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification
This paper describes our system for aspect- based sentiment analysis (ABSA). We partic- ipate in Subtask 1 (sentence-level ABSA), fo- cusing specifically on aspect category detec- tion. We train a …...
XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis
This paper presents our contribution to the Se- mEval 2016 task 5: Aspect-Based Sentiment Analysis. We have addressed Subtask 1 for the restaurant domain, in English and French, which implies …...
ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain
AbstractThis paper describes our systems submitted to the Fine-Grained Sentiment Analysis on Financial Microblogs and News task (i.e., Task 5) in SemEval-2017. This task includes two subtasks in microblogs and …...
ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns
AbstractThis paper describes our submissions to task 7 in SemEval 2017, i.e., Detection and Interpretation of English Puns. We participated in the first two subtasks, which are to detect and …...
Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language
AbstractThis paper describes our system for detection and interpretation of English puns. We participated in 2 subtasks related to homographic puns achieve comparable results for these tasks. Through the paper …...
Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator
AbstractThis paper describes our participation in SemEval-2017 Task 10. We competed in Subtask 1 and 2 which consist respectively in identifying all the key phrases in scientific publications and label …...
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base
AbstractThis paper shows the details of our system submissions in the task 2 of SemEval 2017. We take part in the subtask 1 of this task, which is an English …...
RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks
AbstractIn this paper, we present our systems for the “SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News”. In our system, we combined hand-engineered lexical, sentiment and metadata …...
SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity
AbstractThis paper introduces a new task on Multilingual and Cross-lingual SemanticThis paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word …...
Digital Operatives at SemEval-2018 Task 8: Using dependency features for malware NLP
AbstractThe four sub-tasks of SecureNLP build towards a capability for quickly highlighting critical information from malware reports, such as the specific actions taken by a malware sample. Digital Operatives (DO) …...
DM_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features
AbstractThis paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP. The DM_NLP participated in two subtasks: SubTask 1 classifies if a sentence is useful …...
EICA Team at SemEval-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji Prediction
AbstractThe advent of social media has brought along a novel way of communication where meaning is composed by combining short text messages and visual enhancements, the so-called emojis. We describe …...
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic
AbstractWhile significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic. In fact, businesses are …...
Flytxt_NTNU at SemEval-2018 Task 8: Identifying and Classifying Malware Text Using Conditional Random Fields and Naïve Bayes Classifiers
AbstractCybersecurity risks such as malware threaten the personal safety of users, but to identify malware text is a major challenge. The paper proposes a supervised learning approach to identifying malware …...
KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets
AbstractWe take a multi-task learning approach to the shared Task 1 at SemEval-2018. The general idea concerning the model structure is to use as little external data as possible in …...
Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning
AbstractIn this paper, we describe our proposal for the task of Semantic Extraction from Cybersecurity Reports. The goal is to explore if natural language processing methods can provide relevant and …...
SemEval-2019 Task 12: Toponym Resolution in Scientific Papers
AbstractWe present the SemEval-2019 Task 12 which focuses on toponym resolution in scientific articles. Given an article from PubMed, the task consists of detecting mentions of names of places, or …...
ACNLP at SemEval-2020 Task 6: A Supervised Approach for Definition Extraction
AbstractWe describe our contribution to two of the subtasks of SemEval 2020 Task 6, DeftEval: Extracting term-definition pairs in free text. The system for Subtask 1: Sentence Classification is based …...
Amobee at SemEval-2020 Task 7: Regularization of Language Model Based Classifiers
AbstractThis paper describes Amobee’s participation in SemEval-2020 task 7: “Assessing Humor in Edited News Headlines”, sub-tasks 1 and 2. The goal of this task was to estimate the funniness of …...
BLCU-NLP at SemEval-2020 Task 5: Data Augmentation for Efficient Counterfactual Detecting
AbstractCounterfactuals describe events counter to facts and hence naturally involve common sense, knowledge, and reasoning. SemEval 2020 task 5 is focusing on this field. We participate in the subtask 1 …...
DCC-Uchile at SemEval-2020 Task 1: Temporal Referencing Word Embeddings
AbstractWe present a system for the task of unsupervised lexical change detection: given a target word and two corpora spanning different periods of time, automatically detects whether the word has …...
Ferryman at SemEval-2020 Task 3: Bert with TFIDF-Weighting for Predicting the Effect of Context in Word Similarity
AbstractWord similarity is widely used in machine learning applications like searching engine and recommendation. Measuring the changing meaning of the same word between two different sentences is not only a …...
Ferryman at SemEval-2020 Task 7: Ensemble Model for Assessing Humor in Edited News Headlines
AbstractNatural language processing (NLP) has been applied to various fields including text classification and sentiment analysis. In the shared task of assessing the funniness of edited news headlines, which is …...
Hasyarasa at SemEval-2020 Task 7: Quantifying Humor as Departure from Expectedness
AbstractThis paper describes our system submission Hasyarasa for the SemEval-2020 Task-7: Assessing Humor in Edited News Headlines. This task has two subtasks. The goal of Subtask 1 is to predict …...
HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection
AbstractWe describe our system for Task 5 of SemEval 2020: Modelling Causal Reasoning in Language: Detecting Counterfactuals. Despite deep learning has achieved significant success in many fields, it still hardly …...
Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity
AbstractIn this paper, we present our system for SemEval-2020 task 3, Predicting the (Graded) Effect of Context in Word Similarity. Due to the unsupervised nature of the task, we concentrated …...
JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models against Commonsense Validation and Explanation
AbstractIn this paper, we describe our team’s (JUSTers) effort in the Commonsense Validation and Explanation (ComVE) task, which is part of SemEval2020. We evaluate five pre-trained Transformer-based language models with …...
LMML at SemEval-2020 Task 7: Siamese Transformers for Rating Humor in Edited News Headlines
AbstractThis paper contains a description of my solution to the problem statement of SemEval 2020: Assessing the Funniness of Edited News Headlines. I propose a Siamese Transformer based approach, coupled …...
Pheonix at SemEval-2020 Task 5: Masking the Labels Lubricates Models for Sequence Labeling
AbstractThis paper presents the deep-learning model that is submitted to the SemEval-2020 Task 5 competition: “Detecting Counterfactuals”. We participated in both Subtask1 and Subtask2. The model proposed in this paper …...
RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection
AbstractThis paper describes the model proposed and submitted by our RIJP team to SemEval 2020 Task1: Unsupervised Lexical Semantic Change Detection. In the model, words are represented by Gaussian distributions. …...
SemEval-2020 Task 5: Counterfactual Recognition
AbstractWe present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen …...
YNU-oxz at SemEval-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons LSTM and Hierarchical Attention Network
AbstractThis paper describes the system and results of our team’s participation in SemEval-2020 Task5: Modelling Causal Reasoning in Language: Detecting Counterfactuals, which aims to simulate counterfactual semantics and reasoning in …...
Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction
AbstractThis paper revisits feature engineering approaches for predicting the complexity level of English words in a particular context using regression techniques. Our best submission to the Lexical Complexity Prediction (LCP) …...
CSECU-DSG at SemEval-2021 Task 6: Orchestrating Multimodal Neural Architectures for Identifying Persuasion Techniques in Texts and Images
AbstractInscribing persuasion techniques in memes is the most impactful way to influence peoples’ mindsets. People are more inclined to memes as they are more stimulating and convincing and hence memes …...
Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and Offense
AbstractThis paper introduces the result of Team Grenzlinie’s experiment in SemEval-2021 task 7: HaHackathon: Detecting and Rating Humor and Offense. This task has two subtasks. Subtask1 includes the humor detection …...
MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion
AbstractWe describe our systems of subtask1 and subtask3 for SemEval-2021 Task 6 on Detection of Persuasion Techniques in Texts and Images. The purpose of subtask1 is to identify propaganda techniques …...
PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning
AbstractThis paper describes the winning system for subtask 2 and the second-placed system for subtask 1 in SemEval 2021 Task 4: ReadingComprehension of Abstract Meaning. We propose to use pre-trianed …...
SemEval-2021 Task 1: Lexical Complexity Prediction
AbstractThis paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al. …...
TA-MAMC at SemEval-2021 Task 4: Task-adaptive Pretraining and Multi-head Attention for Abstract Meaning Reading Comprehension
AbstractThis paper describes our system used in the SemEval-2021 Task4 Reading Comprehension of Abstract Meaning, achieving 1st for subtask 1 and 2nd for subtask 2 on the leaderboard. We propose …...
Tsia at SemEval-2021 Task 7: Detecting and Rating Humor and Offense
AbstractThis paper describes our contribution to SemEval-2021 Task 7: Detecting and Rating Humor and Of-fense.This task contains two sub-tasks, sub-task 1and sub-task 2. Among them, sub-task 1 containsthree sub-tasks, sub-task …...
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning
AbstractThis paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process …...
Domain Curricula for Code-Switched MT at MixMT 2022
AbstractIn multilingual colloquial settings, it is a habitual occurrence to compose expressions of text or speech containing tokens or phrases of different languages, a phenomenon popularly known as code-switching or …...
HITMI&T at SemEval-2022 Task 4: Investigating Task-Adaptive Pretraining And Attention Mechanism On PCL Detection
AbstractThis paper describes the system for the Semeval-2022 Task4 ”Patronizing and Condescending Language Detection”.An entity engages in Patronizing and Condescending Language(PCL) when its language use shows a superior attitude towards …...
HW-TSC at SemEval-2022 Task 3: A Unified Approach Fine-tuned on Multilingual Pretrained Model for PreTENS
AbstractIn the paper, we describe a unified system for task 3 of SemEval-2022. The task aims to recognize the semantic structures of sentences by providing two nominal arguments and to …...
MUCS@MixMT: IndicTrans-based Machine Translation for Hinglish Text
AbstractCode-mixing is the phenomena of mixing various linguistic units such as paragraphs, sentences, phrases, words, etc., of one language with that of the other language in any text. This code-mixing …...
PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection
AbstractPatronizing and condescending language (PCL) has a large harmful impact and is difficult to detect, both for human judges and existing NLP systems. At SemEval-2022 Task 4, we propose a …...
Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks
AbstractThis paper introduces the related work and the results of Team Sapphire’s system for SemEval-2022 Task 4: Patronizing and Condescending Language Detection. We only participated in subtask 1. The task …...
SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models
AbstractThis paper describes the Stevens Institute of Technology’s submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask 1 Hindi/English to Hinglish …...
Team LEGO at SemEval-2022 Task 4: Machine Learning Methods for PCL Detection
AbstractIn this paper, we present our submission to the SemEval 2022 - Task 4 on Patronizing and Condescending Language (PCL) detection. Weapproach this problem as a traditional text classification problem …...
Team Stanford ACMLab at SemEval 2022 Task 4: Textual Analysis of PCL Using Contextual Word Embeddings
AbstractWe propose the use of a contextual embedding based-neural model on strictly textual inputs to detect the presence of patronizing or condescending language (PCL). We finetuned a pre-trained BERT model …...
Tesla at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Transformer-based Models with Data Augmentation
AbstractThis paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language (PCL) Detection. For sub-task 1, where the objective is to classify a text as PCL …...
Billie-Newman at SemEval-2023 Task 5: Clickbait Classification and Question Answering with Pre-Trained Language Models, Named Entity Recognition and Rule-Based Approaches
AbstractIn this paper, we describe the implementations of our systems for the SemEval-2023 Task 5 ‘Clickbait Spoiling’, which involves the classification of clickbait posts in sub-task 1 and the spoiler …...
FMI-SU at SemEval-2023 Task 7: Two-level Entailment Classification of Clinical Trials Enhanced by Contextual Data Augmentation
AbstractThe paper presents an approach for solving SemEval 2023 Task 7 - identifying the inference relation in a clinical trials dataset. The system has two levels for retrieving relevant clinical …...
FramingFreaks at SemEval-2023 Task 3: Detecting the Category and the Framing of Texts as Subword Units with Traditional Machine Learning
AbstractThis paper describes our participation as team FramingFreaks in the SemEval-2023 task 3 “Category and Framing Predictions in online news in a multi-lingual setup.” We participated in subtasks 1 and …...
FTD at SemEval-2023 Task 3: News Genre and Propaganda Detection by Comparing Mono- and Multilingual Models with Fine-tuning on Additional Data
AbstractWe report our participation in the SemEval-2023 shared task on propaganda detection and describe our solutions with pre-trained models and their ensembles. For Subtask 1 (News Genre Categorisation), we report …...
Gallagher at SemEval-2023 Task 5: Tackling Clickbait with Seq2Seq Models
AbstractThis paper presents the systems and approaches of the Gallagher team for the SemEval-2023 Task 5: Clickbait Spoiling. We propose a method to classify the type of spoiler (phrase, passage, …...
HHU at SemEval-2023 Task 3: An Adapter-based Approach for News Genre Classification
AbstractThis paper describes our approach for Subtask 1 of Task 3 at SemEval-2023. In this subtask, task participants were asked to classify multilingual news articles for one of three classes: …...
HW-TSC at SemEval-2023 Task 7: Exploring the Natural Language Inference Capabilities of ChatGPT and Pre-trained Language Model for Clinical Trial
AbstractIn this paper, we describe the multi strategy system for SemEval-2022 Task 7, This task aims to determine whether a given statement is supported by one or two Clinical Trial …...
lasigeBioTM at SemEval-2023 Task 7: Improving Natural Language Inference Baseline Systems with Domain Ontologies
AbstractClinical Trials Reports (CTRs) contain highly valuable health information from which Natural Language Inference (NLI) techniques determine if a given hypothesis can be inferred from a given premise. CTRs are …...
SheffieldVeraAI at SemEval-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification
AbstractThis paper describes our approach for SemEval- 2023 Task 3: Detecting the category, the fram- ing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 …...
Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting Online News Genre, Framing and Persuasion Techniques
AbstractThis paper describes the participation of team QUST in the SemEval2023 task3. The monolingual models are first evaluated with the under-sampling of the majority classes in the early stage of …...