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2 results for “hierarchical text classification”
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus
<p>We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores $\simeq 0.85$ on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction.</p>
Hierarchical Text Classification corpora
<p>A set of 3 datasets for Hierarchical Text Classification (HTC), with samples divided into training and testing splits. The hierarchies of labels within all datasets have depth 2.</p> <ul> <li>The <strong>Amazon5x5</strong> dataset contains 500,000 user reviews tagged with the reviewed product's categories. There are 5 product categories with 100,000 examples each, and each category has 5 sub-categories.</li> <li>The <strong>Bugs</strong> dataset contains 30,050 bugs of the Linux kernel, labeled with exactly two categories identifying the affected component.</li> <li>Finally, the <strong>Web Of Science</strong> dataset contains 46,960 abstracts of scientific papers, labeled the article's domain (see <a href="https://data.mendeley.com/datasets/9rw3vkcfy4/6">original repo</a> for more details).</li> </ul> <p>Datasets are published in JSONL format, where each line is a string formatted as a JSON, like in the example below.</p> <pre><code>{ "text": <article text>, "labels": [<label1>, <label2>, ...] }</code></pre> <p>The <em>hierarchical structure</em> of labels in each dataset is documented in <a href="https://gitlab.com/distration/dsi-nlp-publib/-/tree/main/htc-survey-24/data/taxonomies">this repository</a>.</p> <p> </p> <p>These datasets have been presented in this paper:</p> <ul> <li>"Hierarchical Text Classification and its Foundations: a Review of Current Research" - DOI: <a href="https://doi.org/10.3390/electronics13071199">10.3390/electronics13071199</a></li> </ul> <p>Some of these datasets have also been used in:</p> <ul> <li>"Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios" - DOI: <a href="https://doi.org/10.1016/j.eswa.2023.119984">10.1016/j.eswa.2023.119984</a></li> <li>"A multi-level approach for hierarchical Ticket Classification", accepted at WNUT 2022 - <a href="https://aclanthology.org/2022.wnut-1.22/">link</a></li> </ul> <p> </p> <p>These datasets are partially derived from previous work, namely:</p> <ul> <li>[Amazon] J. Ni, J. Li, J. McAuley, "Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects", EMNLP 2019, doi: <a href="http://dx.doi.org/10.18653/v1/D19-1018">10.18653/v1/D19-1018</a></li> <li>[WOS] K. Kowsari, D. E. Brown, M. Heidarysafa, K. Jafari Meimandi, M. S. Gerber and L. E. Barnes, "HDLTex: Hierarchical Deep Learning for Text Classification," 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017, pp. 364-371, doi: <a href="http://doi.org/10.1109/ICMLA.2017.0-134">10.1109/ICMLA.2017.0-134</a></li> <li>[Linux Bugs] V. Lyubinets, T. Boiko and D. Nicholas, "Automated Labeling of Bugs and Tickets Using Attention-Based Mechanisms in Recurrent Neural Networks," <em>2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP)</em>, 2018, pp. 271-275, doi: <a href="http://doi.org/10.1109/DSMP.2018.8478511">10.1109/DSMP.2018.8478511</a></li> </ul>
ScienceDex guides
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