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Unsupervised quantification of entity consistency between photos and text in real-world news ...
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Müller-Budack, Eric. - : Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2022
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Mining an English-Chinese parallel Dataset of Financial News
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In: Journal of Open Humanities Data; Vol 8 (2022); 9 ; 2059-481X (2022)
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A Corpus-Based Sentence Classifier for Entity–Relationship Modelling
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In: Electronics; Volume 11; Issue 6; Pages: 889 (2022)
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Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification
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In: Sensors; Volume 22; Issue 5; Pages: 1899 (2022)
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An Enhanced Neural Word Embedding Model for Transfer Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 2848 (2022)
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Comparative Study of Multiclass Text Classification in Research Proposals Using Pretrained Language Models
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4522 (2022)
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| 8 |
Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms
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In: Information; Volume 13; Issue 3; Pages: 152 (2022)
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Capability Language Processing (CLP): Classification and Ranking of Manufacturing Suppliers Based on Unstructured Capability Data
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| 11 |
StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence
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Analysis and classification of privacy-sensitive content in social media posts
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Team LIA/LS2N at BioCreative VII LitCovid Track: Multi-label Document Classification for COVID-19 Literature using Keyword Based Enhancement and Few-Shot Learning
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In: BioCreative VII Challenge Evaluation Workshop ; https://hal.archives-ouvertes.fr/hal-03426326 ; BioCreative VII Challenge Evaluation Workshop, Nov 2021, Virtual Conference, United States (2021)
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| 14 |
Contextualized, Metadata-Empowered, Coarse-to-Fine Weakly-Supervised Text Classification
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Hate speech and offensive language detection using transfer learning approaches ; Détection du discours de haine et du langage offensant utilisant des approches de Transfer Learning
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In: https://tel.archives-ouvertes.fr/tel-03276023 ; Document and Text Processing. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAS007⟩ (2021)
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| 16 |
Comparison of Deep Learning Approaches for Protective Behaviour Detection Under Class Imbalance from MoCap and EMG data
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In: ACIIW 2021 - 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos ; https://hal.archives-ouvertes.fr/hal-03523502 ; ACIIW 2021 - 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, Sep 2021, Nara, Japan. pp.01-08, ⟨10.1109/ACIIW52867.2021.9666417⟩ ; http://www.casapaganini.it/entimement/workshops/2021/Workshop2021_Home.php (2021)
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| 17 |
К ВОПРОСУ О ТИПОЛОГИИ ТЕКСТА КАК КОГНИТИВНО-РЕЧЕВОГО ПРОИЗВЕДЕНИЯ ... : ON THE QUESTION OF TEXT TYPOLOGY AS A COGNITIVE SPEECH WORK ...
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Bert-Enhanced Text Graph Neural Network for Classification
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In: Entropy ; Volume 23 ; Issue 11 (2021)
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Abstract:
Text classification is a fundamental research direction, aims to assign tags to text units. Recently, graph neural networks (GNN) have exhibited some excellent properties in textual information processing. Furthermore, the pre-trained language model also realized promising effects in many tasks. However, many text processing methods cannot model a single text unit’s structure or ignore the semantic features. To solve these problems and comprehensively utilize the text’s structure information and semantic information, we propose a Bert-Enhanced text Graph Neural Network model (BEGNN). For each text, we construct a text graph separately according to the co-occurrence relationship of words and use GNN to extract text features. Moreover, we employ Bert to extract semantic features. The former part can take into account the structural information, and the latter can focus on modeling the semantic information. Finally, we interact and aggregate these two features of different granularity to get a more effective representation. Experiments on standard datasets demonstrate the effectiveness of BEGNN.
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Keyword:
Bert; graph neural networks; text classification
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URL: https://doi.org/10.3390/e23111536
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