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检索条件"主题词=Hierarchical Text Classification"

共 46 条记录，以下是21-30 订阅

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Intelligent Funds Assistant Exploiting hierarchical text classification Algorithms

Intelligent Funds Assistant Exploiting Hierarchical Text Cla...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Saraiva, Ines Moniz, Daniela Almeida, Alexandre Sousa, Joao Vieira, Susana Axians Digital Consulting Funds & Investment Lisbon Portugal Axians Digital Consulting Coinnovat Grp Lisbon Portugal Inst Super Tecn IDMEC Lisbon Portugal

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

The need to ensure that all funding projects made available by the European Union are fully exploited by each country leads to a robust investigation of the main difficulties of applying to these projects. In this context, Portugal has verified that these funds are not being fully used and detected that one of the main setbacks in the European Union funding application system is the identification of the best call for application. Therefore, this work aims at developing a funds assistant framework to suggest the most suitable set of available calls for application based on the written description of the beneficiaries' projects, via the use of Natural Language Processing and text classification algorithms. Moreover, this problem is addressed as a hierarchical multi-class text classification, taking advantage of the hierarchical structure inside the European funds and aiming to improve the overall performance of the proposed framework. In this context, four models are chosen to compare the classification performance: Naive Bayes, Support Vector Machines, Random Forest and k-Nearest Neighbors. Simulation results highlight that the Support Vector Machine outperforms the rest of the algorithms for almost all classification scenarios. In order to enhance the results, the models are also calibrated to return the second prediction when the likelihood of the first prediction is small, achieving even higher performances for all levels of the hierarchy.

关键词： Machine Learning Natural Language Processing hierarchical text classification Support Vector Machines TF-IDF Vectorization

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A Study on hierarchical text classification as a Seq2seq Task 46th

A Study on Hierarchical Text Classification as a Seq2seq Tas...

引用

46th European Conference on Information Retrieval (ECIR)

作者： Torba, Fatos Gravier, Christophe Laclau, Charlotte Kammoun, Abderrhammen Subercaze, Julien AItenders St Etienne France CNRS Lab Hubert Curien UMR 5516 St Etienne France Inst Polytech Paris Telecom Paris Paris France

ISBN: (纸本)9783031560620;9783031560637

With the progress of generative neural models, hierarchical text classification (HTC) can be cast as a generative task. In this case, given an input text, the model generates the sequence of predicted class labels taken from a label tree of arbitrary width and depth. Treating HTC as a generative task introduces multiple modeling choices. These choices vary from choosing the order for visiting the class tree and therefore defining the order of generating tokens, choosing either to constrain the decoding to labels that respect the previous level predictions, up to choosing the pre-trained Language Model itself. Each HTC model therefore differs from the others from an architectural standpoint, but also from the modeling choices that were made. Prior contributions lack transparent modeling choices and open implementations, hindering the assessment of whether model performance stems from architectural or modeling decisions. For these reasons, we propose with this paper an analysis of the impact of different modeling choices along with common model errors and successes for this task. This analysis is based on an open framework coming along this paper that can facilitate the development of future contributions in the field by providing datasets, metrics, error analysis toolkit and the capability to readily test various modeling choices for one given model.

关键词： hierarchical text classification generative model reproducibility

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COPHTC: CONTRASTIVE LEARNING WITH PROMPT TUNING FOR hierarchical text classification 49

COPHTC: CONTRASTIVE LEARNING WITH PROMPT TUNING FOR HIERARCH...

引用

49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Cai, Fuhan Zhang, Zhongqiang Liu, Duo Fang, Xiangzhong Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

hierarchical text classification (HTC) is an essential yet challenging task in natural language processing (NLP) due to its complex label structure. Recently, a number of approaches have employed prompt learning in HTC, achieving noteworthy outcomes. However, prompt-based HTC does not further optimize the representation of samples based on label relationships, nor does it dynamically adjust the relative positions of sample pairs from the perspective of the embedding space. In this work, we propose a COntrastive-enhanced Prompt-based model for HTC tasks (COPHTC). More specifically, we integrate contrastive learning into prompt tuning while employing momentum updates and a dynamic queue to offer a greater variety of positive and negative samples for the input texts. Extensive experiments on three public datasets verify the effectiveness of COPHTC.

关键词： hierarchical text classification prompt tuning contrastive learning

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Experiments with hierarchical text classification

Experiments with hierarchical text classification

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9th IASTED International Conference on Artificial Intelligence and Soft Computing

作者： Granitzer, M Auer, P Know Ctr Div Knowledge Discovery A-8010 Graz Austria

ISBN: (纸本)0889865361

This paper applies Boosting to hierarchical text classification where the hierarchical structure is given as directed acyclic graph and compares the results to Support Vector Machines. hierarchical classification is performed top-down and in each node a flat classifier decides if a document should be further propagated or not. As flat classifiers Boostexter, CentroidBooster and Support Vector Machines are used, were CentroidBooster is an *** based alternative similar to Boostexter. Experiments on the Reuters Corpus Volume 1 and the OHSUMED data set show that the F-1-measure increases if the hierarchal structure of a data set is taken into account. Regarding time complexity we show, that depending on the structure of a hierarchy, learning and classification time can be reduced. Besides these hard classification approaches we also investigate the ranking performance of hierarchical classifiers. Ranking, which can be achieved by providing a meaningful score for each classification decision, is important in most practical settings. We investigate an approach based on using a sigmoid function for calculating a meaningful score, where parameter estimation is based on error bounds from computational learning theory.

关键词： machine learning supervised learning hierarchical text classification boosting ranking performance

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Efficient Path Prediction for Semi-Supervised and Weakly Supervised hierarchical text classification 19

Efficient Path Prediction for Semi-Supervised and Weakly Sup...

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World Wide Web Conference (WWW)

作者： Xiao, Huiru Liu, Xin Song, Yangqiu Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781450366748

hierarchical text classification has many real-world applications. However, labeling a large number of documents is costly. In practice, we can use semi-supervised learning or weakly supervised learning (e.g., dataless classification) to reduce the labeling cost. In this paper, we propose a path cost-sensitive learning algorithm to utilize the structural information and further make use of unlabeled and weakly-labeled data. We use a generative model to leverage the large amount of unlabeled data and introduce path constraints into the learning algorithm to incorporate the structural information of the class hierarchy. The posterior probabilities of both unlabeled and weakly labeled data can be incorporated with path-dependent scores. Since we put a structure-sensitive cost to the learning algorithm to constrain the classification consistent with the class hierarchy and do not need to reconstruct the feature vectors for different structures, we can significantly reduce the computational cost compared to structural output learning. Experimental results on two hierarchical text classification benchmarks show that our approach is not only effective but also efficient to handle the semi-supervised and weakly supervised hierarchical text classification.

关键词： hierarchical text classification Semi-Supervised Learning Weakly Supervised Learning Structured Prediction

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A method of the feature selection in hierarchical text classification based on the category discrimination and position information

A method of the feature selection in hierarchical text class...

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International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration

作者： Song, Jia Zhang, Pengzhou Qin, Sijun Gong, Junpeng Commun Univ China Fac Sci & Technol Beijing Peoples R China Commun Univ China New Media Inst Beijing Peoples R China

ISBN: (纸本)9781467383127

Feature dimension reduction is an important part in text categorization, and it even becomes more important for child classification in hierarchical text classification. It is presented that Chinese text feature selection method based on category distinction and feature location information in this paper. Experimental results show that the proposed method has a higher precision and recall rate than the others. Therefore the effect of the feature selection is better.

关键词： feature selection category discrimination information gain hierarchical text classification

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Large-Scale hierarchical text classification with Recursively Regularized Deep Graph-CNN 18

Large-Scale Hierarchical Text Classification with Recursivel...

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27th World Wide Web (WWW) Conference

作者： Peng, Hao Li, Jianxin He, Yu Liu, Yaopeng Bao, Mengjiao Wang, Lihong Song, Yangqiu Yang, Qiang Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China HKUST Dept Comp Sci & Engn Clear Water Bay Hong Kong Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China

ISBN: (纸本)9781450356398

text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results. However, when there are a lot of labels with different topical granularities, bag-of-words representation may not be enough. Deep learning models have been proven to be effective to automatically learn different levels of representations for image data. It is interesting to study what is the best way to represent texts. In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-of-words representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels. Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models.

关键词： hierarchical text classification Recursive Regularization Graph-of-words Deep Learning Deep Convolutional Neural Networks

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S²-HTC: hierarchical text classification via Fusing the Structural and Semantic Information 29th

S<SUP>2</SUP>-HTC: Hierarchical Text Classification via Fusi...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Shen, Yinghan Yan, Yu Yin, Dechun Shen, Huawei Chinese Acad Sci Inst Comp Technol Key Lab AI Safety Beijing Peoples R China Peoples Publ Secur Univ China Sch Informat & Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9789819755684;9789819755691

hierarchical text classification (HTC) provides a robust mechanism for systematic text categorization, addressing diverse requirements of text understanding and retrieval. A key issue in HTC is the feature learning for long-tail distributed labels through the use of label relations. Most current HTC approaches mainly focus on label structures, overlooking the intricate semantic details of long-tail labels. This often leads to inadequate modeling of these less frequent, but semantically rich labels, compromising the overall classification accuracy. In this study, we propose the method named S-2-HTC: hierarchical text classification via fusing the Structural and Semantic Information(S-2-HTC), which achieves hierarchical classification by leveraging a method incorporating label structural and semantics relations with the balancing loss calculation. Specifically, S-2-HTC introduces the Label Semantic-Aware and hierarchical Adjacency Matrix (LSA-HAM) to simultaneously capture and integrate the hierarchical and semantic associations of labels. Realizing the natural challenge of long-tailed label distributions in HTC, S-2-HTC adopts the balanced loss computation to efficiently represent low-level label features. Experimental results demonstrate that S-2-HTC outperforms state-of-the-art approaches.

关键词： Natural language processing hierarchical text classification Label semantic modeling

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BCC: BIDIRECTIONAL CONSISTENCY CONSTRAINT METHOD FOR hierarchical text classification 49

BCC: BIDIRECTIONAL CONSISTENCY CONSTRAINT METHOD FOR HIERARC...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shen, Yinghan Yan, Yu Yin, Dechun Shen, Huawei Chinese Acad Sci Inst Comp Technol Key Lab AI Safety & Secur Beijing Peoples R China Peoples Publ Secur Univ China Sch Informat & Cyber Secur Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

hierarchical text classification (HTC) is a useful tool for document categorization based on the taxonomic hierarchy. However, current HTC methods treat labels under each category separately, which makes it difficult to model multiple inheritance labels. To solve this problem, we propose the Bidirectional Consistency Constraint (BCC) method. BCC aims to better handle multiple inheritance and class imbalance by ensuring hierarchy-compliant text-tolabel mapping through relation consistency constraints and balancing loss calculation. BCC generates hierarchical features using the Multidimensional Directed Acyclic Graph (MDAG) and the Hierarchy Alignment Operator (HAO). Additionally, BCC balances the loss calculation, enabling it to fully learn the features of low-level classes (typical minor classes in HTC). Experimental results have shown that BCC outperforms state-of-the-art approaches.

关键词： hierarchical text classification Multiple Inheritance Hierarchy Representation

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Label Hierarchy Alignment for Improved hierarchical text classification

Label Hierarchy Alignment for Improved Hierarchical Text Cla...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kumar, Ashish Toshniwal, Durga Indian Institute of Technology Roorkee Department of Computer Science and Engineering Roorkee India

ISBN: (纸本)9798350324457

Effective text and label representations are crucial for accurate predictions in hierarchical text classification (HTC). However, existing methods face challenges in capturing relevant label representations due to the discrete nature of labels and the complexities of their hierarchical relationships. To address these challenges, we propose Label Hierarchy Alignment (LHA), which ensures that learned label representations conform to the hierarchical label space. In this paper, we present two LHA approaches. The first is adversarial label alignment, which utilizes adversarial learning to enforce a prior distribution on label embeddings. This guides the model to adjust label embeddings in accordance with the hierarchical structure. The second approach is contrastive label alignment, which uses contrastive learning to identify semantic similarities and differences between label nodes across the hierarchy. This enriches the label embeddings with semantic depth, thereby enhancing their alignment with the hierarchical structure. By explicitly addressing various hierarchical aspects, both approaches generate refined label representations that align more effectively with the label hierarchy. We implemented LHA on the current state-of-the-art model and evaluated its performance on two benchmark datasets. The experiment results show that performing LHA improves HTC performance. © 2023 IEEE.

关键词： hierarchical text classification Multi-Label classification Representation Learning

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