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Word-class embeddings for multiclass text classification

DATA MINING AND KNOWLEDGE DISCOVERY 2021年第3期35卷 911-963页

作者： Moreo, Alejandro Esuli, Andrea Sebastiani, Fabrizio CNR Ist Sci & Tecnol Informaz I-56124 Pisa Italy

Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambiguation, machine translation, and sentiment analysis, to name a few. In supervised tasks such as multiclass text classification (the focus of this article) it seems appealing to enhance word representations with ad-hoc embeddings that encode task-specific information. We propose (supervised) word-class embeddings (WCEs), and show that, when concatenated to (unsupervised) pre-trained word embeddings, they substantially facilitate the training of deep-learning models in multiclass classification by topic. We show empirical evidence that WCEs yield a consistent improvement in multiclass classification accuracy, using six popular neural architectures and six widely used and publicly available datasets for multiclass text classification. One further advantage of this method is that it is conceptually simple and straightforward to implement. Our code that implements WCEs is publicly available at https:// ***/AlexMoreo/word-class-embeddings.

关键词： Word-class embeddings Word embeddings Distributional hypothesis multiclass text classification Neural text classification

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Theory-Guided multiclass text classification in Online Academic Discussions

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JOURNAL OF COMPUTER INFORMATION SYSTEMS 2024年

作者： Eryilmaz, Evren Thoms, Brian Ahmed, Zafor Calif State Univ Sacramento CA USA Calif State Univ Channel Isl Camarillo CA USA Univ Houston Downtown Houston TX USA

Machine learning (ML) and deep learning (DL) provide significant opportunities to enhance our understanding of idea generation in asynchronous online discussions (AODs). Drawing on the interaction analysis model (IAM) as our theoretical framework, we built one baseline ML and three DL systems to automate message classification when assessing collaborative knowledge construction depth in academic AODs. The viability of these systems was demonstrated via four offerings of a traditional online course. We achieved 79% as the highest overall accuracy score across all phases of the IAM. To the best of our knowledge, this study is the first to classify AOD messages across all IAM phases. We contribute to the theory by updating the IAM to better explain how to promote deeper interactions in AODs. Additionally, we provide a methodological blueprint for future research where classifying text is crucial.

关键词： Asynchronous online discussions collaborative knowledge construction deep learning machine learning multiclass text classification

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Classifying the Information Needs of Survivors of Domestic Violence in Online Health Communities Using Large Language Models: Prediction Model Development and Evaluation Study

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Journal of Medical Internet Research 2025年第1期27卷 e65397页

作者： Guan, Shaowei Hui, Vivian Stiglic, Gregor Constantino, Rose Eva Lee, Young Ji Wong, Arkers Kwan Ching Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong Centre for Smart Health School of Nursing The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Health and Community Systems School of Nursing University of Pittsburgh Pittsburgh PA United States Faculty of Health Sciences University of Maribor Maribor Slovenia School of Nursing The Hong Kong Polytechnic University Hung Hom Hong Kong

Background: Domestic violence (DV) is a significant public health concern affecting the physical and mental well-being of numerous women, imposing a substantial health care burden. However, women facing DV often encounter barriers to seeking in-person help due to stigma, shame, and embarrassment. As a result, many survivors of DV turn to online health communities as a safe and anonymous space to share their experiences and seek support. Understanding the information needs of survivors of DV in online health communities through multiclass classification is crucial for providing timely and appropriate support. Objective: The objective was to develop a fine-tuned large language model (LLM) that can provide fast and accurate predictions of the information needs of survivors of DV from their online posts, enabling health care professionals to offer timely and personalized assistance. Methods: We collected 294 posts from Reddit subcommunities focused on DV shared by women aged ≥18 years who self-identified as experiencing intimate partner violence. We identified 8 types of information needs: shelters/DV centers/agencies;legal;childbearing;police;DV report procedure/documentation;safety planning;DV knowledge;and communication. Data augmentation was applied using GPT-3.5 to expand our dataset to 2216 samples by generating 1922 additional posts that imitated the existing data. We adopted a progressive training strategy to fine-tune GPT-3.5 for multiclass text classification using 2032 posts. We trained the model on 1 class at a time, monitoring performance closely. When suboptimal results were observed, we generated additional samples of the misclassified ones to give them more attention. We reserved 184 posts for internal testing and 74 for external validation. Model performance was evaluated using accuracy, recall, precision, and F₁-score, along with CIs for each metric. Results: Using 40 real posts and 144 artificial intelligence–generated posts as the test dat

关键词： artificial intelligence domestic violence generative artificial intelligence help seeking information needs large language models multiclass text classification online health communities

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Using of Transformers Models for text classification to Mobile Educational Applications

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IEEE LATIN AMERICA TRANSACTIONS 2023年第6期21卷 730-736页

作者： Garrido, Anabel Pilicita Arias, Enrique Barra Univ Politecn Madrid Madrid Spain

In Q2 2022, educational apps were the second most popular category on the Google Play Store, accounting for 10.47% of the apps available worldwide. This work explores the application of five BERT-based pre-trained models with the Transformers architecture to classify mobile educational applications. These five models are according to the knowledge field: bert-base-cased, bert-base-uncased, roberta-base, albert-base-v2 and distilbert-base-uncased. This study uses a dataset with educational apps of Google Play, this dataset was enriched with description and category because it lacked this information. In all models, a tokenizer and fine-tuning works were applied for training in the classification task. After training the data, the testing phase was performed in which the models had to go through four training epochs to obtain better results: roberta-base with 81% accuracy, bert-base-uncased with 79% accuracy, bert-base-cased obtained 80% accuracy, albert-base-v2 obtained 78% accuracy and distilbert-base-uncased obtained 76% accuracy.

关键词： Bit error rate Transformers Internet Training text categorization Recurrent neural networks IEEE transactions Natural Language Processing multiclass text classification Bidirectional Encoder Representations from Transformers

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Leveraging AI in Service Automation Modeling: From Classical AI Through Deep Learning to Combination Models 17th

Leveraging AI in Service Automation Modeling: From Classical...

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17th International Conference on Service-Oriented Computing (ICSOC)

作者： Wang, Qing Shwartz, Larisa Grabarnik, Genady Ya Nidd, Michael Hwang, Jinho IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA St Johns Univ Dept Math & Comp Sci Queens NY 11439 USA IBM Res Zurich CH-8803 Ruschlikon Switzerland

ISBN: (纸本)9783030337025;9783030337018

With the advent of cloud, new generations of digital services are being conceived to respond to the ever-growing demands and expectations of the market place. In parallel, automations are becoming an essential enabler for successful management of these services. With such importance being placed on digital services, automated management of these services - in particular, automated incident resolution - becomes a key issue for both the provider and the users. The challenge facing automation providers lies in variability and the frequently changing nature of the monitoring tickets that provide the primary input to automation. Despite the value of the correct automation at the correct time, it is also important to remember that triggering an incorrect automation may damage the smooth operation of the business. In this paper, we discuss AI modeling for automation recommendations. We describe a wide range of experiments which allowed us to conclude an optimal method with respect to accuracy and speed acceptable to service providers.

关键词： Classical and deep learning models Combination models multiclass text classification AI for service automation

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On Label Quality in Class Imbalance Setting A Case Study 21

On Label Quality in Class Imbalance Setting A Case Study

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Alshehri, Jumanah Stanojevic, Marija Dragut, Eduard Obradovic, Zoran Temple Univ Ctr Data Analyt & Biomed Informat Philadelphia PA 19122 USA

ISBN: (纸本)9781665462839

Producing high-quality labeled data is a challenge in any supervised learning problem, where in many cases, human involvement is necessary to ensure the label quality. However, human annotations are not flawless, especially in the case of a challenging problem. In nontrivial problems, the high disagreement among annotators results in noisy labels, which affect the performance of any machine learning model. In this work, we consider three noise reduction strategies to improve the label quality in the Article-Comment Alignment Problem, where the main task is to classify article-comment pairs according to their relevancy level. The first considered labeling disagreement reduction strategy utilizes annotators' background knowledge during the label aggregation step. The second strategy utilizes user disagreement during the training process. In the third and final strategy, we ask annotators to perform corrections and relabel the examples with noisy labels. We deploy these strategies and compare them to a resampling strategy for addressing the class imbalance, another common supervised learning challenge. These alternatives were evaluated on ACAP, a multiclass text pairs classification problem with highly imbalanced data, where one of the classes represents at most 15% of the dataset's entire population. Our results provide evidence that considered strategies can reduce disagreement between annotators. However, data quality improvement is insufficient to enhance classification accuracy in the article-comment alignment problem, which exhibits a high-class imbalance. The model performance is enhanced for the same problem by addressing the imbalance issue with a weight loss-based class distribution resampling. We show that allowing the model to pay more attention to the minority class during the training process with the presence of noisy examples improves the test accuracy by 3%.

关键词： Label quality annotators disagreement data imbalance problem multiclass text classification

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Spider Plus: A text Classifier for Research Article Components 8

Spider Plus: A Text Classifier for Research Article Componen...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Bahala, Renante G. Sagum, Ria A. Polytechnic University of the Philippines Philippines College of Computer and Information Sciences Polytechnic University of the Philippines Philippines

Uploading research articles to a database can be a complex process with multiple HTML fields. The complexity of this process makes users less efficient and productive. This study explored the use of the Multilayer Perceptron (MLP) algorithm using language-independent features (count and Boolean features) to create a multi-class text classifier that can classify 10 research article components (e.g., title, authors, abstract, etc.). The text classifier was developed to pave the way for a streamlined manual data entry process for uploading research articles to a database by implementing a single textarea in favor of using multiple HTML fields. The text samples were obtained from multiple sources using web scraping technology, consolidated, cleaned, and standardized. The 12 language-independent features were generated based on the textual formats of sample texts (e.g., count of capitalized letters, digits, punctuation, checking the existence of a URL pattern, etc.). Recursive feature elimination with cross validation (RFECV) was used to determine the optimal number of input features. The hyperparameter values of the model were determined through the grid search technique. A trial-and-error process was conducted to determine the number of hidden layers. The model achieved 95%, 94%, and 95% scores for micro, macro, and weighted average f1-scores, respectively. The model performed well in classifying research article components. However, it is sensitive to textual format (e.g., lower or upper case, punctuation used, etc.). For subsequent research in this area, this study recommends investigating the use of both language-dependent and language-independent features to address the limitations of the current model. © 2023 The Authors. Published by Elsevier B.V.

关键词： artificial neural network language-independent features multiclass text classification multilayer perceptron research article components

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AI Model for Industry classification Based on Website Data

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INFORMATION 2024年第2期15卷 89页

作者： Jagric, Timotej Herman, Aljaz Univ Maribor Fac Econ & Business Inst Finance & Artificial Intelligence Razlagova 14 Maribor 2000 Slovenia

This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting in a consistent decrease in training loss, indicative of the model's learning efficacy. Subsequent validation on a separate dataset revealed the model's robust performance, with classification accuracies ranging from 83.5% to 92.6% across different industry classes. Our model showed a high overall accuracy of 88.23%, coupled with a robust F1 score of 0.88. These results highlight the model's ability to capture and utilize the nuanced features of text data pertinent to various industries. The model has the capability to harness real-time web data, thereby enabling the utilization of the latest and most up-to-date information affecting to the company's product portfolio. Based on the model's performance and its characteristics, we believe that the process of relative valuation can be drastically improved.

关键词： industry classification BERT transformer business descriptions multiclass text classification AI

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Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques

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JMIR FORMATIVE RESEARCH 2022年第9期6卷 e36118页

作者： Kabir, Muhammad Khubayeeb Islam, Maisha Kabir, Anika Nahian Binte Haque, Adiba Rhaman, Md Khalilur Brac Univ Dept Comp Sci 66 Mohakhali Dhaka 1212 Bangladesh Brac Univ Dept Comp Sci & Engn Dhaka Bangladesh

Background: There are a myriad of language cues that indicate depression in written texts, and natural language processing (NLP) researchers have proven the ability of machine learning and deep learning approaches to detect these cues. However, to date, these approaches bridging NLP and the domain of mental health for Bengali literature are not comprehensive. The Bengali-speaking population can express emotions in their native language in greater detail. Objective: Our goal is to detect the severity of depression using Bengali texts by generating a novel Bengali corpus of depressive posts. We collaborated with mental health experts to generate a clinically sound labeling scheme and an annotated corpus to train machine learning and deep learning models. Methods: We conducted a study using Bengali text-based data from blogs and open source platforms. We constructed a procedure for annotated corpus generation and extraction of textual information from Bengali literature for predictive analysis. We developed our own structured data set and designed a clinically sound labeling scheme with the help of mental health professionals, adhering to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) during the process. We used 5 machine learning models for detecting the severity of depression: kernel support vector machine (SVM), random forest, logistic regression K-nearest neighbor (KNN), and complement naive Bayes (NB). For the deep learning approach, we used long short-term memory (LSTM) units and gated recurrent units (GRUs) coupled with convolutional blocks or self-attention layers. Finally, we aimed for enhanced outcomes by using state-of-the-art pretrained language models. Results: The independent recurrent neural network (RNN) models yielded the highest accuracies and weighted F1 scores. GRUs, in particular, produced 81% accuracy. The hybrid architectures could not surpass the RNNs in terms of performance. Kernel SVM with term frequency-inver

关键词： mental health forums natural language processing severity major depressive disorder deep learning machine learning multiclass text classification

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