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ADCluster: Adaptive Deep Clustering for unsupervised learning from unlabeled documents
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-6791-8284
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0001-8820-2405
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0001-7349-7693
2023 (English)In: Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023) / [ed] Mourad Abbas; Abed Alhakim Freihat, Association for Computational Linguistics, 2023, p. 68-77Conference paper, Published paper (Refereed)
Abstract [en]

We introduce ADCluster, a deep document clustering approach based on language models that is trained to adapt to the clustering task. This adaptability is achieved through an iterative process where K-Means clustering is applied to the dataset, followed by iteratively training a deep classifier with generated pseudo-labels – an approach referred to as inner adaptation. The model is also able to adapt to changes in the data as new documents are added to the document collection. The latter type of adaptation, outer adaptation, is obtained by resuming the inner adaptation when a new chunk of documents has arrived. We explore two outer adaptation strategies, namely accumulative adaptation (training is resumed on the accumulated set of all documents) and non-accumulative adaptation (training is resumed using only the new chunk of data). We show that ADCluster outperforms established document clustering techniques on medium and long-text documents by a large margin. Additionally, our approach outperforms well-established baseline methods under both the accumulative and non-accumulative outer adaptation scenarios.

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2023. p. 68-77
Keywords [en]
deep clustering, adaptive, deep learning, unsupervised, data stream
National Category
Computer Sciences
Research subject
Computer Science; computational linguistics
Identifiers
URN: urn:nbn:se:umu:diva-220260OAI: oai:DiVA.org:umu-220260DiVA, id: diva2:1833048
Conference
6th International Conference on Natural Language and Speech Processing (ICNLSP 2023), Online, December 16-17, 2023.
Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-03-20Bibliographically approved
In thesis
1. Deep learning for news topic identification in limited supervision and unsupervised settings
Open this publication in new window or tab >>Deep learning for news topic identification in limited supervision and unsupervised settings
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Djup maskininlärning för identifiering av nyhetsämnen i inlärningssituationer med begränsad eller ingen övervakning
Abstract [en]

In today's world, following news is crucial for decision-making and staying informed. With the growing volume of daily news, automated processing is essential for timely insights and in aiding individuals and corporations in navigating the complexities of the information society. Another use of automated processing is contextual advertising, which addresses privacy concerns associated with cookie-based advertising by placing ads solely based on web page content, without tracking users or their online behavior. Therefore, accurately determining and categorizing page content is crucial for effective ad placements. The news media, heavily reliant on advertising to sustain operations, represent a substantial market for contextual advertising strategies.

Inspired by these practical applications and the advancements in deep learning over the past decade, this thesis mainly focuses on using deep learning for categorizing news articles into topics of varying granularity. Considering the dynamic nature of these applications and the limited availability of relevant labeled datasets for training models, the thesis emphasizes developing methods that can be trained effectively using unlabeled or partially labeled data. It proposes semi-supervised text classification models for categorizing datasets into predefined coarse-grained topics, where only a few labeled examples exist for each topic, while the majority of the dataset remains unlabeled. Furthermore, to better explore coarse-grained topics within news archives and streams and overcome the limitations of predefined topics in text classification the thesis suggests deep clustering approaches that can be trained in unsupervised settings. 

Moreover, to address the identification of fine-grained topics, the thesis introduces a novel story discovery model for monitoring event-based topics in multi-source news streams. Given that online news reporting often incorporates diverse modalities like text, images, video, and audio to convey information, the thesis finally initiates an investigation into the synergy between textual and visual elements in news article analysis. To achieve this objective, a text-image dataset was annotated, and a baseline was established for event-topic discovery in multimodal news streams. While primarily intended for news monitoring and contextual advertising, the proposed models can, more generally, be regarded as novel approaches in semi-supervised text classification, deep clustering, and news story discovery. Comparison with state-of-the-art baseline models demonstrates their effectiveness in addressing the respective objectives.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2024. p. 62
Series
Report / UMINF, ISSN 0348-0542 ; 24.04
Keywords
Topic Identification, Data Clustering, News Stream Clustering, Semi-Supervised Learning, Unsupervised Learning, Event Topics, News Stories, Multimodal News, Document Classification, Document Clustering, Deep Learning, Deep Clustering, Pre-trained Language Models
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-222534 (URN)9789180703420 (ISBN)9789180703437 (ISBN)
Public defence
2024-04-16, MIT.A.121, MIT-huset, Umeå, 13:15 (English)
Opponent
Supervisors
Available from: 2024-03-26 Created: 2024-03-20 Last updated: 2024-03-22Bibliographically approved

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fulltext(810 kB)54 downloads
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Hatefi, ArezooVu, Xuan-SonBhuyan, Monowar H.Drewes, Frank

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