Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training (2024.emnlp-main)
Copied to clipboard
| Challenge: | Emojis have gained immense popularity on social media platforms, serving as a common means to supplement or replace text. |
| Approach: | They propose a graph pre-train framework for text and emoji co-modeling that incorporates two tasks: node-level graph contrastive learning and edge-level link reconstruction learning. |
| Outcome: | The proposed framework improves on the Xiaohongshu and Twitter datasets with two types of downstream tasks. |
Similar Papers
Incorporating Emoji Descriptions Improves Tweet Classification (N19-1)
Copied to clipboard
| Challenge: | Tweets are short messages that often include specialized language such as hashtags and emojis. |
| Approach: | They propose a simple strategy to replace emojis with their natural language description and use pretrained word embeddings to process tweets. |
| Outcome: | The proposed method is more effective than pretrained emoji embeddings for tweet classification. |
Exploring Graph Pre-training for Aspect-based Sentiment Analysis (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies tend to extract the sentiment elements in a generative manner to avoid complex modeling of sentiment elements. |
| Approach: | They propose a generative model with an Element-level Graph Pre-training paradigm and a Task Decomposition Pre- training paradigm to make it generalizable and robust against irregular sentiment quadruples. |
| Outcome: | The proposed model is generalizable and robust against irregular sentiment quadruples. |
Classifying the Informative Behaviour of Emoji in Microblogs (L18-1)
Copied to clipboard
| Challenge: | Emoji are pictographs used in microblogs as emotion markers, but can also represent a wider range of concepts. |
| Approach: | They analyze a corpus of tweets pairs and classify emoji with respect to redundancy . they propose to further investigate the informative behaviour of e-mails using eoji . |
| Outcome: | The proposed model achieved an F-score of 0.7 for emoji use in 2475 tweets pairs. |
Pretraining Language Models with Text-Attributed Heterogeneous Graphs (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing pretraining tasks for Language Models (LMs) focus on learning the textual information of each entity and overlook the crucial aspect of capturing topological connections among entities in TAHGs. |
| Approach: | They propose a topology-aware pretraining task that explicitly considers the topological and heterogeneous information in TAHGs by optimizing an LM and an auxiliary heterogenous graph neural network. |
| Outcome: | The proposed framework explicitly considers the topological and heterogeneous information in TAHGs. |
Pre-trained Semantic Interaction based Inductive Graph Neural Networks for Text Classification (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for text classification have vanishing or exploding gradients when dealing with long sequences, making it difficult to handle long-distance dependencies. |
| Approach: | They propose a graph neural network based on pre-trained semantic interaction called PaSIG . they construct a text-word heterogeneity graph and use context representation capability . |
| Outcome: | The proposed model outperforms existing methods on five datasets and achieves state-of-the-art performance. |
EmoTag1200: Understanding the Association between Emojis and Emotions (2020.emnlp-main)
Copied to clipboard
| Challenge: | Emojis are increasingly used to convey affect, but their use is not trivial. |
| Approach: | They propose to use human-solicited association ratings to explore the connection between emojis and emotions to conduct experiments. |
| Outcome: | The proposed method can be inferred from word-level information when high-quality information is available. |
What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)
Copied to clipboard
| Challenge: | Existing datasets for irony detection only contain 10% of ironic tweets with emojis . 45% of internet users in the united states use an e-moji in social media . |
| Approach: | They propose to use emojis to analyze irony detection datasets to train classifiers. |
| Outcome: | The proposed pipeline can be used to analyze irony detection datasets using emojis. |
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings (2021.emnlp-main)
Copied to clipboard
| Challenge: | Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddable sentences labelled as semantically similar by annotators. |
| Approach: | They propose a language-independent approach to build large datasets of pairs of informal texts weakly similar, without manual human effort, exploiting Twitter’s powerful signals of relatedness: replies and quotes of tweets. |
| Outcome: | The proposed model learns classical Semantic Textual Similarity, and excels on tasks where pairs of sentences are not exact paraphrases. |
Leveraging Training Dynamics and Self-Training for Text Classification (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Semi-supervised learning (SSL) is a promising technique for improving deep learning models when training data is scarce. |
| Approach: | They propose a semi-supervised learning approach that leverages training dynamics of unlabeled data. |
| Outcome: | The proposed method achieves an average increase in F1 score of 3.5% over baselines in low resource settings. |
Multi-resolution Annotations for Emoji Prediction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Emojis are able to express various linguistic components, such as emotions, sentiments, events, etc. emojis have the merit of preserving information more densely, compared to words, argues a new study. |
| Approach: | They propose to use passage-level and aspect-level emoji annotations to predict the proper emmojis associated with text. |
| Outcome: | The proposed method is heuristically generated and validated with a pre-trained BERT model. |