HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification

• URL: http://arxiv.org/abs/2204.08115v1
• Date: Mon, 18 Apr 2022 00:57:46 GMT
• Title: HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification
• Authors: Pengfei Gao, Jingpeng Zhao, Yinglong Ma, Ahmad Tanvir, Beihong Jin
• Abstract summary: Hierarchical multi-label text classification (HMTC) with higher accuracy over large sets of closely related categories has become a challenging problem. We present a hierarchical and fine-tuning approach based on the Ordered Neural LSTM neural network, abbreviated as HFT-ONLSTM, for more accurate level-by-level HMTC.
• Score: 7.176984223240199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many important classification problems in the real-world consist of a large number of closely related categories in a hierarchical structure or taxonomy. Hierarchical multi-label text classification (HMTC) with higher accuracy over large sets of closely related categories organized in a hierarchy or taxonomy has become a challenging problem. In this paper, we present a hierarchical and fine-tuning approach based on the Ordered Neural LSTM neural network, abbreviated as HFT-ONLSTM, for more accurate level-by-level HMTC. First, we present a novel approach to learning the joint embeddings based on parent category labels and textual data for accurately capturing the joint features of both category labels and texts. Second, a fine tuning technique is adopted for training parameters such that the text classification results in the upper level should contribute to the classification in the lower one. At last, the comprehensive analysis is made based on extensive experiments in comparison with the state-of-the-art hierarchical and flat multi-label text classification approaches over two benchmark datasets, and the experimental results show that our HFT-ONLSTM approach outperforms these approaches, in particular reducing computational costs while achieving superior performance.

Related papers

• Balanced Classification: A Unified Framework for Long-Tailed Object Detection [74.94216414011326]
  Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. We introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution. BACL consistently achieves performance improvements across various datasets with different backbones and architectures.
  arXiv  Detail & Related papers  (2023-08-04T09:11:07Z)
• Semantic Guided Level-Category Hybrid Prediction Network for Hierarchical Image Classification [8.456482280676884]
  Hierarchical classification (HC) assigns each object with multiple labels organized into a hierarchical structure. We propose a novel semantic guided level-category hybrid prediction network (SGLCHPN) that can jointly perform the level and category prediction in an end-to-end manner.
  arXiv  Detail & Related papers  (2022-11-22T13:49:10Z)
• Many-Class Text Classification with Matching [65.74328417321738]
  We formulate textbfText textbfClassification as a textbfMatching problem between the text and the labels, and propose a simple yet effective framework named TCM. Compared with previous text classification approaches, TCM takes advantage of the fine-grained semantic information of the classification labels.
  arXiv  Detail & Related papers  (2022-05-23T15:51:19Z)
• Decision Making for Hierarchical Multi-label Classification with Multidimensional Local Precision Rate [4.812468844362369]
  We introduce a new statistic called the multidimensional local precision rate (mLPR) for each object in each class. We show that classification decisions made by simply sorting objects across classes in descending order of their mLPRs can, in theory, ensure the class hierarchy. In response, we introduce HierRank, a new algorithm that maximizes an empirical version of CATCH using estimated mLPRs while respecting the hierarchy.
  arXiv  Detail & Related papers  (2022-05-16T17:43:35Z)
• CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification [70.554573538777]
  We argue for hierarchical evaluation of the predictions of neural LMTC models. We describe a structural issue in the representation of the structured label space in prior art. We propose a set of metrics for hierarchical evaluation using the depth-based representation.
  arXiv  Detail & Related papers  (2021-09-10T13:09:12Z)
• Multitask Learning for Class-Imbalanced Discourse Classification [74.41900374452472]
  We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP.
  arXiv  Detail & Related papers  (2021-01-02T07:13:41Z)
• Coherent Hierarchical Multi-Label Classification Networks [56.41950277906307]
  C-HMCNN(h) is a novel approach for HMC problems, which exploits hierarchy information in order to produce predictions coherent with the constraint and improve performance. We conduct an extensive experimental analysis showing the superior performance of C-HMCNN(h) when compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2020-10-20T09:37:02Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)
• Joint Embedding of Words and Category Labels for Hierarchical Multi-label Text Classification [4.2750700546937335]
  hierarchical text classification (HTC) has received extensive attention and has broad application prospects. We propose a joint embedding of text and parent category based on hierarchical fine-tuning ordered neurons LSTM (HFT-ONLSTM) for HTC.
  arXiv  Detail & Related papers  (2020-04-06T11:06:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.