There are 3 repositories under semeval topic.
Ekphrasis is a text processing tool, geared towards text from social networks, such as Twitter or Facebook. Ekphrasis performs tokenization, word normalization, word segmentation (for splitting hashtags) and spell correction, using word statistics from 2 big corpora (english Wikipedia, twitter - 330mil english tweets).
An implementation of a full named-entity evaluation metrics based on SemEval'13 Task 9 - not at tag/token level but considering all the tokens that are part of the named-entity
Deep-learning model presented in "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis".
[WWW 2022] KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
TensorFlow Implementation of the paper "End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures" and "Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths" for classifying relations
Winner system (DAMO-NLP) of SemEval 2022 MultiCoNER shared task over 10 out of 13 tracks.
Semantic Textual Similarity (STS) measures the degree of equivalence in the underlying semantics of paired snippets of text.
Deep-learning models of NTUA-SLP team submitted in SemEval 2018 tasks 1, 2 and 3.
Relation Classification - SEMEVAL 2010 task 8 dataset
The Codebase for Quasi-Attention BERT Model for TABSA Tasks (AAAI '21)
Sentiment Analysis: Deep Bi-LSTM+attention model
Sentiment Analysis with Deep Learning models. Implemented with Tensorflow and Keras.
SemEval-2018 Task 12: The Argument Reasoning Comprehension Task
Deep-learning system presented in "EmoSence at SemEval-2019 Task 3: Bidirectional LSTM Network for Contextual Emotion Detection in Textual Conversations" at SemEval-2019.
Taxonomy refinement method to improve domain-specific taxonomy systems.
SemEval 2024 Task 1 : Textual Semantic Relatedness
Deep-learning model presented in "DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison".
Sarcasm is a term that refers to the use of words to mock, irritate, or amuse someone. It is commonly used on social media. The metaphorical and creative nature of sarcasm presents a significant difficulty for sentiment analysis systems based on affective computing. The technique and results of our team, UTNLP, in the SemEval-2022 shared task 6 on sarcasm detection are presented in this paper.
Code and data used for participation in SemEval-2018 Task 3: "Irony detection in English tweets"
The PreTENS shared task hosted at SemEval 2022 aims at focusing on semantic competence with specific attention on the evaluation of language models with respect to the recognition of appropriate taxonomic relations between two nominal arguments (i.e. cases where one is a supercategory of the other, or in extensional terms, one denotes a superset of the other).
The code and data accompanying the ACL 2017 "outstanding award" publication "Vancouver Welcomes You! Minimalist Location Metonymy Resolution"
Sentence Based Sentiment Analysis
基于Bert的文本情感分析模型(含semeval14数据集)
Our submission to the SemEval2019 shared task on Hyperpartisan News Detection.
Course Project | COL772 (NLP) @ IIT Delhi
Tools for Evaluation of Unsupervised Word Sense Disambiguation Systems
This repo is part of code implementation for "SENN: Stock Ensemble-based Neural Network"
Code for 3 papers: 1) "Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets"; 2) "LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest neighbor Classification for Sarcasm Detection"; 3) "Fuzzy Rough Nearest Neighbour Methods for Detecting Emotions, Hate Speech and Irony" by O. Kaminska, Ch. Cornelis and V. Hoste.