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Found 26 papers, 3 papers with code
YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles
This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques.
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis
In recent years, the growing ubiquity of Internet memes on social media platforms, such as Facebook, Instagram, and Twitter, has become a topic of immense interest.
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis
Deep neural network models such as long short-term memory (LSTM) and tree-LSTM have been proven to be effective for sentiment analysis.
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.
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-HPCC at SemEval-2019 Task 8: Using A LSTM-Attention Model for Fact-Checking in Community Forums
The LSTM-Attention model uses two LSTM to extract the features of the question and answer pair.
YNU-HPCC at SemEval-2019 Task 6: Identifying and Categorising Offensive Language on Twitter
This document describes the submission of team YNU-HPCC to SemEval-2019 for three Sub-tasks of Task 6: Sub-task A, Sub-task B, and Sub-task C. We have submitted four systems to identify and categorise offensive language.
YUN-HPCC at SemEval-2019 Task 3: Multi-Step Ensemble Neural Network for Sentiment Analysis in Textual Conversation
This paper describes our approach to the sentiment analysis of Twitter textual conversations based on deep learning.
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-HPCC at SemEval-2019 Task 9: Using a BERT and CNN-BiLSTM-GRU Model for Suggestion Mining
Consumer opinions towards commercial entities are generally expressed through online reviews, blogs, and discussion forums.
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-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on Twitter
This paper describe the system we proposed to participate the first year of Irony detection in English tweets competition.
YNU-HPCC at SemEval-2018 Task 12: The Argument Reasoning Comprehension Task Using a Bi-directional LSTM with Attention Model
This paper introduces a bi-directional long short term memory (Bi-LSTM) with an attention model to select a correct warrant from two to explain an argument.
YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense Knowledge
Typically, there are several questions for each text that correspond to it.
YNU-HPCC at SemEval-2018 Task 2: Multi-ensemble Bi-GRU Model with Attention Mechanism for Multilingual Emoji Prediction
This paper describes our approach to SemEval-2018 Task 2, which aims to predict the most likely associated emoji, given a tweet in English or Spanish.
YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in Tweets
The useful BiLSTM (Bidirectional Long-Short Term Memory) model with attention mechanism was mainly applied for our system.
YNU-HPCC at IJCNLP-2017 Task 4: Attention-based Bi-directional GRU Model for Customer Feedback Analysis Task of English
This paper describes our submission to IJCNLP 2017 shared task 4, for predicting the tags of unseen customer feedback sentences, such as comments, complaints, bugs, requests, and meaningless and undetermined statements.
YNU-HPCC at IJCNLP-2017 Task 5: Multi-choice Question Answering in Exams Using an Attention-based LSTM Model
A shared task is a typical question answering task that aims to test how accurately the participants can answer the questions in exams.
YNU-HPCC at IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis Using a Bi-directional LSTM-CRF Model
Building a system to detect Chinese grammatical errors is a challenge for natural-language processing researchers.
Refining Word Embeddings for Sentiment Analysis
Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks.
YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction
In this paper, we present a system that uses a convolutional neural network with long short-term memory (CNN-LSTM) model to complete the task.
YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classification
In this paper, we propose a multi-channel convolutional neural network-long short-term memory (CNN-LSTM) model that consists of two parts: multi-channel CNN and LSTM to analyze the sentiments of short English messages from Twitter.
Chinese Grammatical Error Diagnosis Using Single Word Embedding
A computer-assisted learning tool which can automatically detect and correct Chinese grammatical errors is necessary for those foreign students.
