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Cformer: Semi-Supervised Text Clustering Based on Pseudo Labeling
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundation of Language Processing)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
2021 (English)In: CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, ACM Digital Library, 2021, p. 3078-3082Conference paper, Published paper (Refereed)
Abstract [en]

We propose a semi-supervised learning method called Cformer for automatic clustering of text documents in cases where clusters are described by a small number of labeled examples, while the majority of training examples are unlabeled. We motivate this setting with an application in contextual programmatic advertising, a type of content placement on news pages that does not exploit personal information about visitors but relies on the availability of a high-quality clustering computed on the basis of a small number of labeled samples.

To enable text clustering with little training data, Cformer leverages the teacher-student architecture of Meta Pseudo Labels. In addition to unlabeled data, Cformer uses a small amount of labeled data to describe the clusters aimed at. Our experimental results confirm that the performance of the proposed model improves the state-of-the-art if a reasonable amount of labeled data is available. The models are comparatively small and suitable for deployment in constrained environments with limited computing resources. The source code is available at https://github.com/Aha6988/Cformer.

Place, publisher, year, edition, pages
ACM Digital Library, 2021. p. 3078-3082
Keywords [en]
meta pseudo clustering, semi-supervised learning, pseudo labeling
National Category
Language Technology (Computational Linguistics) Computer Sciences
Research subject
computational linguistics; Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-186787DOI: 10.1145/3459637.3482073Scopus ID: 2-s2.0-85119178919ISBN: 978-1-4503-8446-9 (print)OAI: oai:DiVA.org:umu-186787DiVA, id: diva2:1590965
Conference
CIKM2021, 30th ACM International Conference on Information and Knowledge Management, Online via Queensland, Australia, November 1-5, 2021
Available from: 2021-09-03 Created: 2021-09-03 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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Hatefi, ArezooVu, Xuan-SonBhuyan, Monowar H.Drewes, Frank

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Citation style
  • apa
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