Label Embedding using Hierarchical Structure of Labels for Twitter Classification (D19-1)
Copied to clipboard
| Challenge: | Twitter is used for disaster monitoring and news material gathering . we propose a method that can consider the hierarchical structure of labels and labels themselves . |
| Approach: | They propose a method that can consider the hierarchical structure of labels and label texts themselves. |
| Outcome: | The proposed method outperforms the methods of the conference participants over the text REtrieval Conference (TREC) 2018 Incident Streams (IS) dataset. |
Similar Papers
Hierarchical Label Generation for Text Classification (2023.findings-eacl)
Copied to clipboard
| Challenge: | None Hierarchical text classification (HTC) aims to assign the most relevant labels with their structure for a given document. |
| Approach: | They propose a method that captures the label hierarchy for real-world classification applications by using a taxonomic hierarchy. |
| Outcome: | The proposed method can generate unseen labels in subword level. |
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for hierarchical text classification focus on modeling the text, but the concept of sharing among classes has been ignored in previous work. |
| Approach: | They propose a concept-based method that explicitly represents the concept and model the sharing mechanism among classes for the hierarchical text classification. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two widely used datasets. |
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods encode text and label hierarchy separately and mix their representations for classification, where the hierarchy remains unchanged for all input text. |
| Approach: | They propose to embed hierarchy into a text encoder by combining input and output data to generate a hierarchy-aware representation. |
| Outcome: | Extensive experiments on three benchmark datasets verify the effectiveness of the proposed model. |
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for hierarchical multi-label classification do not assume label hierarchy exists. |
| Approach: | They propose to jointly learn the classifier parameters as well as the label embeddings . they propose to use hyperbolic embeddables to gain better generalisation over the labels . |
| Outcome: | The proposed method achieves state-of-the-art generalization on benchmarks and is more accurate than existing methods. |
Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods ignore the semantic relationship between text and labels, so they cannot make full use of hierarchical information. |
| Approach: | They propose a hierarchy-aware label semantics matching network to model the semantic relationship between text and labels in a semantic matching problem. |
| Outcome: | The proposed model captures the text-label semantics matching relationship among coarse-grained labels and fine-grain labels in a hierarchy-aware manner. |
Joint Embedding of Words and Labels for Text Classification (P18-1)
Copied to clipboard
Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
| Challenge: | Existing approaches to text classification use word embeddings to capture semantic regularities between words. |
| Approach: | They propose to view text classification as a label-word joint embedding problem . they use a framework that measures compatibility between text sequences and labels . |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on large text datasets. |
Fusing Label Embedding into BERT: An Efficient Improvement for Text Classification (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to improve text classification performance of pre-trained models have been used to improve their performance. |
| Approach: | They propose a method for improving BERT's performance by using a label embedding technique while keeping almost the same computational cost. |
| Outcome: | The proposed method improves BERT's performance on six text classification benchmark datasets while keeping almost the same computational cost. |
Towards Better Hierarchical Text Classification with Data Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data. |
| Approach: | They propose a hierarchical text classification framework that can achieve both label controllability and text diversity by extracting high-quality hierarchic label information. |
| Outcome: | The proposed method can achieve label controllability and text diversity by extracting high-quality hierarchical label information. |
Exploiting Global and Local Hierarchies for Hierarchical Text Classification (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods encode label hierarchy in a global view, which makes them hard to exploit hierarchical information. |
| Approach: | They propose to leverage label hierarchy in multi-label text classification by encoding label hierarchy as a static hierarchical structure containing all labels. |
| Outcome: | The proposed method achieves significant improvement on three benchmark datasets compared with the state-of-the-art method HGCLR. |
Efficient Strategies for Hierarchical Text Classification: External Knowledge and Auxiliary Tasks (2020.acl-main)
Copied to clipboard
| Challenge: | Hierarchical text classification is a complex task that requires extended training time and a large number of parameters. |
| Approach: | They propose a top-up-classification task using dictionaries and auxiliary task from external dictionary definitions. |
| Outcome: | The proposed method outperforms previous studies using a reduced number of parameters in two well-known English datasets. |