Search Results for author:
Found 24 papers, 2 papers with code
Automatic Detecting for Health-related Twitter Data with BioBERT
Social media used for health applications usually contains a large amount of data posted by users, which brings various challenges to NLP, such as spoken language, spelling errors, novel/creative phrases, etc.
Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks
This task can be considered as a task of binary classification of news texts.
Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling
Five methods were applied to generate soft negative samples that shared highly similar text but had different semantics from the original.
hub at SemEval-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical Complexity
Use Inception block as a shared layer to learn sentence and word frequency information We described the implementation of our best system and discussed our methods and experiments in the task.
Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and Offense
Detection task is a binary classification task, and the rating prediction task is a regression task between 0 to 5.
hub at SemEval-2021 Task 2: Word Meaning Similarity Prediction Model Based on RoBERTa and Word Frequency
What we need to do is to use our method to determine as accurately as possible the meaning of the words in a sentence pair are the same or different.
hub at SemEval-2021 Task 5: Toxic Span Detection Based on Word-Level Classification
This article introduces the system description of the hub team, which explains the related work and experimental results of our team{'}s participation in SemEval 2021 Task 5: Toxic Spans Detection.
Lee at SemEval-2020 Task 12: A BERT Model Based on the Maximum Self-ensemble Strategy for Identifying Offensive Language
We only participate in the English part of subtask A, which aims to identify offensive languages in English.
YNUtaoxin at SemEval-2020 Task 11: Identification Fragments of Propaganda Technique by Neural Sequence Labeling Models with Different Tagging Schemes and Pre-trained Language Model
We only participated in the first subtask, and a neural sequence model was used to perform the sequence tagging task.
Zyy1510 Team at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text with Sub-word Level Representations
This ensemble model combines the advantage of rich sequential patterns and the intermediate features after convolution from the LSTM model, and the polarity of keywords from the MNB model to obtain the final sentiment score.
BYteam at SemEval-2020 Task 5: Detecting Counterfactual Statements with BERT and Ensembles
We participate in the classification tasks of SemEval-2020 Task: Subtask1: Detecting counterfactual statements of semeval-2020 task5(Detecting Counterfactuals).
DEEPYANG at SemEval-2020 Task 4: Using the Hidden Layer State of BERT Model for Differentiating Common Sense
Introducing common sense to natural language understanding systems has received increasing research attention.
YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction
We participated in the BioNLP 2019 Open Shared Tasks: binary relation extraction of SeeDev task.
YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language
This paper describes the system submitted to SemEval 2019 Task 6: OffensEval 2019.
YNU\_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification
In this paper we describe a deep-learning system that competed as SemEval 2019 Task 9-SubTask A: Suggestion Mining from Online Reviews and Forums.
YNU\_DYX at SemEval-2019 Task 5: A Stacked BiGRU Model Based on Capsule Network in Detection of Hate
This paper describes our system designed for SemEval 2019 Task 5 {``}Shared Task on Multilingual Detection of Hate{''}. We only participate in subtask-A in English.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension
This paper describes the system submitted to SemEval-2018 Task 12 (The Argument Reasoning Comprehension Task).
Lyb3b at SemEval-2018 Task 12: Ensemble-based Deep Learning Models for Argument Reasoning Comprehension Task
In this task, given a natural language argument with a reason and a claim, the goal is to choose the correct implicit reasoning from two options, in order to form a reasonable structure of (Reason, Warrant, Claim).
Yuan at SemEval-2018 Task 1: Tweets Emotion Intensity Prediction using Ensemble Recurrent Neural Network
We perform the LSTM and BiLSTM model for the emotion intensity prediction.
YNU\_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension
We firstly use GloVe to learn the distributed representations automatically from the instance, question and answer triples.
Lyb3b at SemEval-2018 Task 11: Machine Comprehension Task using Deep Learning Models
This paper is to solve the task 11 of SemEval-2018, Machine Comprehension using Commonsense Knowledge task.
YNUDLG at IJCNLP-2017 Task 5: A CNN-LSTM Model with Attention for Multi-choice Question Answering in Examinations
In this paper, we perform convolutional neural networks (CNN) to learn the joint representations of question-answer pairs first, then use the joint representations as the inputs of the long short-term memory (LSTM) with attention to learn the answer sequence of a question for labeling the matching quality of each answer.
YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter
Sentiment analysis is one of the central issues in Natural Language Processing and has become more and more important in many fields.
