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External methods to address limitations of using global information on the narrow-down approach for hierarchical text classification

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JOURNAL OF INFORMATION SCIENCE 2014年第5期40卷 688-708页

作者： Oh, Heung-Seon Jung, Yuchul Korea Inst Sci & Technol Informat Taejon 305806 South Korea

Classifying documents to a large-scale web taxonomy is a challenging research problem because of a large number of categories and associated documents in the taxonomy. The state-of-the-art solution known as the narrow-down approach utilizes a search engine to reduce an entire category hierarchy to most relevant categories and selects the best one among them using a classifier. In a recent language modelling approach, top-level category information (or global information) was used in judging the appropriateness of a local category, which led to performance improvements. However, we observe that using global information has a limited influence on the final category selection under some conditions. First, global information may be inaccurate even though it is generated by a top-level category classifier using an entire hierarchy. Second, it has little influence when two competing categories share the same top-level category or when the local category information has too strong an influence on the final category selection. To resolve the limitations, in this paper, we propose two external methods: (1) a meta-classifier with novel dependency features among top-level categories based on an ensemble learning framework;and (2) a query modification model based on a statistical feedback method to improve the query document representation instead of just juggling with information in the hierarchy. Our methods were evaluated using the Open Directory Project test collection.

关键词： hierarchical text classification language models web taxonomy

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hierarchical text classification based on LDA and Domain Ontology

Hierarchical Text Classification based on LDA and Domain Ont...

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2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： An, Wei Liu, Qihua Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang Jiangxi Peoples R China

This paper combines domain ontology and LDA model to propose a new method of hierarchical web text classification. Experimental results show that the method has good performance with high recall rate and accuracy rate.

ISBN: (纸本)9783037858646

关键词： text classification Latent Dirichlet allocation Domain ontology hierarchical text classification

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Multi-Granular text classification with Minimal Supervision 24

Multi-Granular Text Classification with Minimal Supervision

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17th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Zhang, Yunyi Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798400703713

Our society has been immersed with massive unstructured text data, posing great challenges for people to fetch needed data, digest critical information, and derive actionable knowledge. Such needs necessitate the development of text classification which is a fundamental task towards structuring the unstructured web data. Existing methods either require heavy human annotation or work only with limited scope (e.g., classification into only a small number of classes), far off from the real needs. Recently developed deep learning and pre-trained language models boost our research substantially, but many problems still remain. Therefore, we propose to develop a minimally-supervised approach to structure massive text into a multi-granularity text space. We explore the following four subtasks: (1) weak supervision enrichment, (2) PLM-enhanced weakly-supervised text classification, (3) empowering fine-grained text classification with enriched taxonomy, (4) joint classification of multi-granular text units.

关键词： Weakly-Supervised text classification hierarchical text classification Zero-Shot Learning Taxonomy Enrichment and Expansion Pre-Trained Language Models

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Label-Guided Graphormer for Hierarchy text classification 32nd

Label-Guided Graphormer for Hierarchy Text Classification

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32nd International Conference on Artificial Neural Networks (ICANN)

作者： Li, Song Huo, Jingjing Xu, Wenqiang Zheng, Maozong Zhang, Kezun Chen, Hong Lin, Xuan AntGrp 556 Xixi Rd Hangzhou Peoples R China

ISBN: (纸本)9783031441912;9783031441929

hierarchical text classification (HTC), a subtask of multilabel classification, remains a challenging problem due to its complex label topology hierarchy. Although various methods have been proposed for modeling hierarchical information in recent research, less attention has been paid to techniques that utilize label information hidden in the training data, which can be potentially beneficial for hierarchical classification. In this paper, we focus on enhancing label representations from the perspective of contextual interactions and semantic complements, which refer to label co-occurrences and keywords respectively. And a Label-guided Graphormer (LGraphormer) is proposed for the purpose of integrating these promising features. The final prediction is performed by feeding its output embeddings into a text encoder in a hierarchyaware manner. Our extensive experiments show that the proposed approach achieves state-of-the-art performance on three widely used public datasets, demonstrating the effectiveness of our methodology. Our code is available at: https://***/ContentTech/***.

关键词： hierarchical text classification data information label represent enhancement Graphormer net

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Zero-Shot Taxonomy Mapping for Document classification 23

Zero-Shot Taxonomy Mapping for Document Classification

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38th Annual ACM Symposium on Applied Computing (ACM SAC)

作者： Bongiovanni, Lorenzo Bruno, Luca Dominici, Fabrizio Rizzo, Giuseppe LINKS Fdn Turin 1 Italy

ISBN: (纸本)9781450395175

classification of documents according to a custom internal hierarchical taxonomy is a common problem for many organizations that deal with textual data. Approaches aimed to address this challenge are, for the vast majority, supervised methods, which have the advantage of producing good results on specific datasets, but the major drawbacks of requiring an entire corpus of annotated documents, and the resulting models are not directly applicable to a different taxonomy. In this paper, we aim to contribute to this important issue, by proposing a method to classify text according to a custom hierarchical taxonomy entirely without the need of labelled data. The idea is to first leverage the semantic information encoded into pre-trained Deep Language Models to assigned a prior relevance score for each label of the taxonomy using zero-shot, and secondly take advantage of the hierarchical structure to reinforce this prior belief. Experiments are conducted on three hierarchically annotated datasets: WebOfScience, DBpedia Extracts and Amazon Product Reviews, which are very diverse in the type of language adopted and have taxonomy depth of two and three levels. We first compare different zero-shot methods, and then we show that our hierarchy-aware approach substantially improves results across every dataset.

关键词： Natural Language Processing Zero-Shot text classification hierarchical text classification

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LH-Mix: Local Hierarchy Correlation Guided Mixup over hierarchical Prompt Tuning 25

LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierar...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Fanshuang Kong Richong Zhang Ziqiao Wang CCSE Beihang University Beijing China Tongji University Shanghai China

ISBN: (纸本)9798400712456

hierarchical text classification (HTC) aims to assign one or more labels in the hierarchy for each text. Many methods represent this structure as a global hierarchy, leading to redundant graph structures. To address this, incorporating a text-specific local hierarchy is essential. However, existing approaches often model this local hierarchy as a sequence, focusing on explicit parent-child relationships while ignoring implicit correlations among sibling/peer relationships. In this paper, we first integrate local hierarchies into a manual depth-level prompt to capture parent-child relationships. We then apply Mixup to this hierarchical prompt tuning scheme to improve the latent correlation within sibling/peer relationships. Notably, we propose a novel Mixup ratio guided by local hierarchy correlation to effectively capture intrinsic correlations. This Local Hierarchy Mixup (LH-Mix) model demonstrates remarkable performance across three widely-used datasets.

关键词： hierarchical text classification

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An Empirical Comparative Study on Two Large-Scale hierarchical text classification Approaches

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International Journal of Computer Processing of Languages 2011年第4期23卷 309-325页

作者： JIAN ZHANG HAI ZHAO LIQING ZHANG BAO-LIANG LU Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiaotong University 800 Dongchuan Rd. Shanghai 200240 China Department of Computer Science Virginia Tech Blacksburg VA 24061 USA

Patent classification is a large scale hierarchical text classification (LSHTC) task. Though comprehensive comparisons, either learning algorithms or feature selection strategies, have been fully made in the text categorization field, few work was done for a LSHTC task due to high computational cost and complicated structural label characteristics. For the first time, this paper compares two popular learning frameworks, namely, hierarchical support vector machine (SVM) and k -nearest neighbor ( k -NN) that are applied to a LSHTC task. Our experimental results show that the latter outperforms the former for the LSHTC task, which is quite different from the existing results for normal text categorization tasks. In addition, this paper compares different similarity measures and ranking strategies in k -NN framework for LSHTC task. From our empirical study, conclusions can be drawn that k -NN is more appropriate for the LSHTC task than hierarchical SVM. BM25 outperforms other similarity measures and ListWeak gains a better performance than other ranking strategies. Our empirical results also indicate that using all the labels of the retrieved neighbors can remarkably improve classification performance over only using the first label of the retrieved neighbors.

关键词： Large-scale text classification hierarchical text classification text classification k -NN hierarchical SVM similarity measure ranking strategies

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An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition

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BMC BIOINFORMATICS 2015年第1期16卷 1-28页

作者： Tsatsaronis, George Balikas, Georgios Malakasiotis, Prodromos Partalas, Ioannis Zschunke, Matthias Alvers, Michael R. Weissenborn, Dirk Krithara, Anastasia Petridis, Sergios Polychronopoulos, Dimitris Almirantis, Yannis Pavlopoulos, John Baskiotis, Nicolas Gallinari, Patrick Artieres, Thierry Ngomo, Axel-Cyrille Ngonga Heino, Norman Gaussier, Eric Barrio-Alvers, Liliana Schroeder, Michael Androutsopoulos, Ion Paliouras, Georgios Tech Univ Dresden Biotechnol Ctr D-01307 Dresden Germany Transinsight GmbH D-01307 Dresden Germany NCSR Demokritos Athens 60228 Greece Athens Univ Econ & Business Athens 10434 Greece Univ Paris 06 F-75005 Paris France Univ Leipzig D-04109 Leipzig Germany Univ Grenoble 1 F-38041 St Martin Dheres France

Background: This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BIOASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. Results: The 2013 BIOASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PUBMED documents with MESH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MESH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BIOASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. Conclusions: A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MESH headings to published articles or to English questions;retrieve relevant RDF triples from ontologies, relevant articles and snippets from PUBMED Central;produce "exact" and paragraph-sized "ideal" answers (summaries). The results of the systems that participated in the 2013 BIOASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM's MTI indexer. In Task 1b the systems received high scores in the man

关键词： BIOASQ Competition hierarchical text classification Semantic indexing Information retrieval Passage retrieval Question answering Multi-document text summarization

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hierarchical text classification Incremental Learning

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16th International Conference on Neural Information Processing (ICONIP 2009)

作者： Song, Shengli Qiao, Xiaofei Chen, Ping Xidian Univ Inst Software Engn Xian 710071 Peoples R China

ISBN: (纸本)9783642106767

To classify large-scale text corpora, an incremental learning method for hierarchical text classification is proposed. Based on the deep analysis of virtual classification tree based hierarchical text classification, combining the two application models of single document adjustment after classification and new sample set learning, a dynamic online learning algorithm and a sample set incremental learning algorithm are put forward. By amending classifiers and updating the feature space, the algorithms improve the current classification models. hierarchical text classification experiments on Newsgroup datasets show that the algorithms can enhance the classification accuracy effectively and reduce the storage space and the learning time cost of the history sample datasets.

关键词： machine learning incremental learning hierarchical text classification text mining

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Geocoding textual Documents Through a Hierarchy of Linear Classifiers 17th

Geocoding Textual Documents Through a Hierarchy of Linear Cl...

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17th Portuguese Conference on Artificial Intelligence (EPIA)

作者： Melo, Fernando Martins, Bruno Univ Lisbon Inst Super Tecn P-1699 Lisbon Portugal Univ Lisbon INESC ID P-1699 Lisbon Portugal

ISBN: (纸本)9783319234854;9783319234847

In this paper, we empirically evaluate an automated technique, based on a hierarchical representation for the Earth's surface and leveraging linear classifiers, for assigning geospatial coordinates to previously unseen documents, using only the raw text as input evidence. We measured the results obtained with models based on Support Vector Machines, over collections of geo-referenced Wikipedia articles in four different languages, namely English, German, Spanish and Portuguese. The best performing models obtained state-of-the-art results, corresponding to an average prediction error of 83 Kilometers, and a median error of just 9 Kilometers, in the case of the English Wikipedia collection.

关键词： text mining Document geocoding hierarchical text classification

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