An Assistant Teaching System of English Creative Writing Based on Semantic Mining

With the continuous evolution of natural language processing (NLP), conversational agents have become pivotal in enhancing user engagement and satisfaction in the e-commerce domain. This paper presents a comprehensive study and implementation of intent recognition and slot filling techniques tailored for an e-commerce chatbot. Leveraging a dataset of 8,000 samples, we employed a Convolutional Neural Network (CNN) combined with Long Short-Term Memory (LSTM) networks for intent recognition, achieving an accuracy of 87%. For slot filling, we utilized the Bidirectional Encoder Representations from Transformers (BERT) model, also attaining an accuracy of 87%. The chatbot seamlessly integrates with an e-commerce database, using OpenAI's GPT-3 to generate natural language responses from query results. Our system demonstrates significant advancements in processing user queries, generating precise database queries, and providing coherent and relevant responses, thereby enhancing the overall user experience. This work aims to equip researchers and practitioners with insights into the methodologies and challenges in developing sophisticated e-commerce chatbots, fostering further innovation in this field. Key Words: Intent Recognition, Slot Filling, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), GPT-3, Natural Language Understanding (NLU), Chatbot

BackgroundIt is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However, the application of recent deep learning techniques to radiology reports, which can improve the detection performance, has not been thoroughly examined. Moreover, free-text that clinicians input in the ordering form (order information) has seldom been used to identify actionable reports. This study aims to evaluate the benefits of two new approaches: (1) bidirectional encoder representations from transformers (BERT), a recent deep learning architecture in NLP, and (2) using order information in addition to radiology reports.MethodsWe performed a binary classification to distinguish actionable reports (i.e., radiology reports tagged as actionable in actual radiological practice) from non-actionable ones (those without an actionable tag). 90,923 Japanese radiology reports in our hospital were used, of which 788 (0.87%) were actionable. We evaluated four methods, statistical machine learning with logistic regression (LR) and with gradient boosting decision tree (GBDT), and deep learning with a bidirectional long short-term memory (LSTM) model and a publicly available Japanese BERT model. Each method was used with two different inputs, radiology reports alone and pairs of order information and radiology reports. Thus, eight experiments were conducted to examine the performance.ResultsWithout order information, BERT achieved the highest area under the precision-recall curve (AUPRC) of 0.5138, which showed a statistically significant improvement over LR, GBDT, and LSTM, and the highest area under the receiver operating characteristic curve (AUROC) of 0.9516. Simply coupling the order information with the radiology reports slightly increased the AUPRC of BERT but did not lead to a statistically significant improvement. This may be due to the complexity of clinical decisions made by radiologists.ConclusionsBERT was assumed to be useful to detect actionable reports. More sophisticated methods are required to use order information effectively.

The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance.

As social media and online social networking sites have emerged as essential channels for everyday social interactions, researchers are increasingly turning to conversational social media as a valuable alternative source of insights into public sentiment and behavior during times of crisis. In a similar vein, our study investigates the utility of using conversational social media data for analyzing public sentiment related to the COVID-19 pandemic. Our research question is, to what extent can machine learning models based on conversational social media data help gauge public sentiment during a pandemic? To evaluate the performance of various machine learning algorithms in sentiment analysis tasks, we used a Twitter dataset and compared their effectiveness using eight different models (i.e., Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Extreme Gradient Boosting (XGBoost), Logistic Regression, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT)). The results show that among the eight models, the top three models are BERT, LSTM, and SVM, achieving overall accuracies of 87.44%, 81.94%, and 80.02%, respectively. Employing sentiment analysis models on pandemic-related social media posts allows us to gain insights into of human emotions and sentiments during public health crises like a pandemic.

Our study focuses on Traditional Chinese Medical (TCM) named entity recognition (NER), which involves identifying and extracting specific entity names from TCM record. This task has significant implications for doctors and researchers, as it enables the automated identification of relevant TCM terms, ultimately enhancing research efficiency and accuracy. However, the current Bidirectional Encoder Representations from Transformers-Long Short Term Memory-Conditional Random Fields (BERT-LSTM-CRF) model for TCM NER is constrained by a traditional structure, limiting its capacity to fully harness the advantages provided by Bidirectional Encoder Representations from Transformers (BERT) and long short term memory (LSTM) models. Through comparative experiments, we also observed that the straightforward superimposition of models actually leads to a decrease in recognition results. To optimize the structure of the traditional BERT-BiLSTM-CRF model and obtain more effective text representations, we propose the Dyn-Att Net model, which introduces dynamic attention and a parallel structure. By integrating BERT and LSTM models with the dynamic attention mechanism, our model effectively captures semantic, contextual, and sequential relations within text sequences, resulting in high accuracy. To validate the effectiveness of our model, we compared it with nine other models in TCM dataset namely the publicly available PaddlePaddle dataset. Our Dyn-Att Net model, based on BERT, outperforms the other models, achieving an F1 score of 81.91%, accuracy of 92.06%, precision of 80.26%, and recall of 83.76%. Furthermore, its robust generalization capability is substantiated through validation on the APTNER, MSRA, and EduNER datasets. Overall, the Dyn-Att Net model not only enhances NER accuracy within the realm of traditional Chinese medicine, but also showcases considerable potential for cross-domain generalization. Moreover, the Dyn-Att Net model's parallel architecture facilitates efficient computation, contributing to time-saving efforts in NER tasks.

Social networks have become indispensable parts of our daily life, and therefore understanding the process of information diffusion over social networks is a meaningful research topic. Usually, multiple pieces of information do not spread in isolation; rather, they interact with each other throughout the diffusion process. This paper aims to quantify these interactions by modeling users' forwarding behavior after reading a series of information. Inspired by several successful components prevalent in recent research of deep learning, i.e., long short term memory (LSTM) network and bi-directional encoder representation from transformers (BERT), we designed IMM Enhanced model and InfoLSTM model. In our experiments on real-world Weibo dataset, both models significantly outperform baselines such as the prior IMM model and IP model, with IMM Enhanced model improving 23.52% and InfoLSTM model improving 32.56% in F1 score (absolute value) compared to that of baseline IMM model. In addition, we visualize the dataset and the parameters learned in IMM Enhanced model, which further enables us to discuss the relationship between text similarity and information diffusion interaction with case studies.

Fenomena bullying di media sosial, khususnya di Twitter, telah menjadi isu yang semakin memprihatinkan dengan dampak signifikan terhadap kesehatan mental pengguna. Dalam rangka mengatasi masalah ini, deteksi otomatis tweet yang mengandung konten bullying menjadi sangat penting. Penelitian ini bertujuan untuk membandingkan performa dua model pemrosesan bahasa alami terbaru, yaitu BERT (Bidirectional Encoder Representations from Transformers) dan XLNet, dalam klasifikasi tweet yang mengandung bullying. Metodologi penelitian ini melibatkan pengumpulan dataset tweet yang telah dilabeli sebagai bullying atau non-bullying. Proses preprocessing teks dilakukan untuk membersihkan dan menyiapkan data sebelum digunakan dalam pelatihan model. Kedua model, BERT dan XLNet, dilatih dan diuji menggunakan dataset yang sama. Evaluasi performa dilakukan dengan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa kedua model memiliki kemampuan yang baik dalam mengidentifikasi tweet bullying, akan tetapi XLNet menunjukkan performa yang lebih unggul dibandingkan BERT dengan tingkat akurasi sebesar 95%. Dengan nilai presisi = 100%, recall = 0,87%, dan F1-score = 0,88%. XLNet mampu menangkap konteks dan nuansa bahasa yang lebih kompleks dalam tweet, yang berkontribusi pada akurasi klasifikasi yang lebih tinggi. Penelitian ini memberikan kontribusi penting dalam bidang deteksi bullying di media sosial dengan menunjukkan bahwa penggunaan model XLNet lebih efektif dibandingkan BERT. Temuan ini dapat membantu platform seperti Twitter dalam mengidentifikasi dan mencegah konten bullying, sehingga menciptakan lingkungan online yang lebih aman bagi pengguna, serta dapat digunakan sebagai dasar untuk pengembangan sistem deteksi bullying yang lebih canggih dan efisien di masa depan. Abstract The phenomenon of bullying on social media, particularly on Twitter, has become an increasingly concerning issue with significant impacts on users' mental health. In order to address this issue, automatic detection of tweets containing bullying content is crucial. This study aims to compare the performance of two recent natural language processing models, namely BERT (Bidirectional Encoder Representations from Transformers) and XLNet, in the classification of tweets containing bullying. The research methodology involves collecting a dataset of tweets that have been labelled as bullying or non-bullying. Text preprocessing is done to clean and prepare the data before it is used in model training. Both models, BERT and XLNet, were trained and tested using the same dataset. Performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that both models have a good ability to identify bullying tweets, but XLNet shows superior performance compared to BERT with an accuracy rate of 95%. With precision = 100%, recall = 0.87%, and F1-score = 0.88%. XLNet is able to capture more complex context and language nuances in tweets, which contributes to higher classification accuracy. This research makes an important contribution to the field of bullying detection on social media by showing that the use of the XLNet model is more effective than BERT. These findings can help platforms like Twitter identify and prevent bullying content, thereby creating a safer online environment for users, and can be used as a basis for the development of more sophisticated and efficient bullying detection systems in the future.

Sentence Boundary Disambiguation (SBD) is crucial for building datasets for tasks such as machine translation, syntactic analysis, and semantic analysis. Currently, most automatic sentence segmentation in Tibetan adopts the methods of rule-based and statistical learning, as well as the combination of the two, which have high requirements on the corpus and the linguistic foundation of the researchers and are more costly to annotate manually. In this study, we explore Tibetan SBD using deep learning technology. Initially, we analyze Tibetan characteristics and various subword techniques, selecting Byte Pair Encoding (BPE) and Sentencepiece (SP) for text segmentation and training the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model. Secondly, we studied the Tibetan SBD based on different BERT pre-trained language models, which mainly learns the ambiguity of the shad (“།”) in different positions in modern Tibetan texts and determines through the model whether the shad (“།”) in the texts has the function of segmenting sentences. Meanwhile, this study introduces four models, BERT-CNN, BERT-RNN, BERT-RCNN, and BERT-DPCNN, based on the BERT model for performance comparison. Finally, to verify the performance of the pre-trained language models on the SBD task, this study conducts SBD experiments on both the publicly available Tibetan pre-trained language model TiBERT and the multilingual pre-trained language model (Multi-BERT). The experimental results show that the F1 score of the BERT (BPE) model trained in this study reaches 95.32% on 465,669 Tibetan sentences, nearly five percentage points higher than BERT (SP) and Multi-BERT. The SBD method based on pre-trained language models in this study lays the foundation for establishing datasets for the later tasks of Tibetan pre-training, summary extraction, and machine translation.

The article examines the use of neural networks in telemedicine for patient self-diagnosis, focusing on custom LSTM (Long Short-Term Memory) models and pre-trained BERT (Bidirectional Encoder Representations from Transformers) models. Telemedicine has become essential in modern healthcare, particularly due to the rise in remote consultations and diagnostics during global health crises. The study reviews research in this area, highlighting contributions from international and Ukrainian experts. It explores the effectiveness of neural networks in interpreting patient symptom descriptions and generating accurate diagnoses. The custom LSTM model processes text-based symptom descriptions but faces challenges with limited datasets, impacting its diagnostic accuracy. On the other hand, the BERT model leverages transfer learning, achieving over 92% accuracy in predictions, thanks to its ability to understand complex language inputs. An enhanced LSTM model is also presented, incorporating Dropout and Batch Normalization layers, which help increase the robustness and accuracy of the predictions while managing the model's complexity. The article also discusses the technical challenges of training these neural networks, including the variability of symptom descriptions and the need for large annotated medical datasets. Proposed solutions include domain-specific enhancements and data augmentation to improve the models' performance. In comparing the three models, the study identifies that while the custom LSTM model is more flexible and simple, it struggles with generalization. The BERT model, by contrast, excels in understanding and accuracy but requires more computational resources. The enhanced LSTM model offers a balance between complexity and performance. In conclusion, neural networks offer promising advances in telemedicine by providing accurate, AI-assisted self-diagnosis. Although challenges remain, such as data requirements and computing power, models like BERT are key to improving healthcare accessibility and accuracy in telemedicine applications. Among the main advantages of using artificial intelligence for the healthcare system are in-depth diagnostic analysis, individual treatment regimens, and optimization of medical processes. However, modern technologies in the medical field are not yet able to completely replace experienced specialists.

Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning

Due to the significant increase in Internet activity since the COVID-19 epidemic, many informal, unstructured, offensive, and even misspelled textual content has been used for online communication through various social media. The Bengali and Banglish(Bengali words written in English format) offensive texts have recently been widely used to harass and criticize people on various social media. Our deep excavation reveals that limited work has been done to identify offensive Bengali texts. In this study, we have engineered a detection mechanism using natural language processing to identify Bengali and Banglish offensive messages in social media that could abuse other people. First, different classifiers have been employed to classify the offensive text as baseline classifiers from real-life datasets. Then, we applied boosting algorithms based on baseline classifiers. AdaBoost is the most effective ensemble method called adaptive boosting, which enhances the outcomes of the classifiers. The long short-term memory (LSTM) model is used to eliminate long-term dependency problems when classifying text, but overfitting problems occur. AdaBoost has strong forecasting ability and overfitting problem does not occur easily. By considering these two powerful and diverse models, we propose L-Boost, the modified AdaBoost algorithm using bidirectional encoder representations from transformers (BERT) with LSTM models. We tested the L-Boost model on three separate datasets, including the BERT pre-trained word-embedding vector model. We find our proposed L-Boost’s efficacy better than all the baseline classification algorithms reaching an accuracy of 95.11%.

Augmenting commit classification by using fine-grained source code changes and a pre-trained deep neural language model

Deepfakes, which are synthetic media realistic in nature generated using artificial intelligence (AI); pose a significant threat to individuals and society. The rapid advancement of deepfake technology has led to the creation of highly realistic synthetic content covering images, videos, audio, and news. While deepfake applications offer creative possibilities, their misuse for misinformation, identity fraud, and cybersecurity threats necessitates robust detection methods. Deepfake crimes are rising daily, wherein deepfake media detection has become a big challenge and has high claim in digital forensics. This review explores the state-of-the-art deep learning (DL) techniques for deepfake detection of four parameters, namely images, videos, audio, and news. The ML approaches rely on handcrafted features but struggle with evolving deepfake methods. In contrast, DL techniques, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have demonstrated superior detection accuracy by learning discriminative features. Even Recurrent Neural Networks (RNNs), and Transformer-based architectures like Bidirectional encoder representations from transformers (BERT), have demonstrated superior accuracy in identifying manipulated content. Furthermore, recent advancements such as Vision Transformers (ViTs) and Explainable AI (XAI) models are enhancing detection interpretability and robustness. This review highlights the future research directions for strengthening deepfake detection mechanisms. The rapid advancements in deepfake generation necessitate continuous research and development of countermeasures.

In the rapidly evolving Internet of Things (IoT) landscape, accurate indoor positioning is increasingly vital. The proposed algorithm synergizes an ultrawideband (UWB) sensor with an inertial measurement unit (IMU) and artificial intelligence to obtain precise positioning in nonline-of-sight (NLOS) scenarios. In the proposed UWB module, a large language model (LLM) such as bidirectional encoder representations from transformers (BERT) algorithm is designed to utilize the channel impulse response (CIR) for effective NLOS identification and UWB ranging trustworthiness evaluation. Concurrently, the IMU module is also designed with BERT to recognize various pedestrian activity states, thereby optimizing positioning. BERT’s self-attention mechanism and deep learning (DL) bidirectional training efficiently extract essential features from sequential data, capturing both local and global information. The integration of both UWB and IMU through a proposed tightly coupled algorithm significantly boosts positioning performance. Experimental campaigns demonstrate an average NLOS identification accuracy, line-of-sight (LOS), and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}2$ </tex-math></inline-formula> of 98.8%, 99.4%, and 0.9926, respectively. These performances surpass the state-of-the-art least-squares support vector machine (LS-SVM), convolutional neural network (CNN), and CNN with long short-term memory (CNN-LSTM) up to 17.66% in NLOS identification. In terms of pedestrian activity recognition using BERT, the BERT algorithm achieves a precision (recall) of 99.3% (99.4%), notably outperforming the CNN and CNN-LSTM by 17.9% (16.2%) and 11.9% (10.9%), respectively. Finally, the UWB-IMU algorithm significantly enhances positioning accuracy by 80.5%, outperforming Kalman, LSTM-EKF, and particle filter (PF) methods by 68.1%, 48.3%, and 45.0%, respectively. The proposed approach presents a robust solution for indoor positioning for IoT applications, particularly in challenging NLOS environments.

Recently, people used social media to convey their emotions in the text. Text classification is required for group a text containing emotion or not and identify the type of emotion. Most of the research in emotion classification use machine learning methods. So this research offers the use of deep learning for the classification of emotions in text. One of the algorithms that is widely used for deep learning is BERT (Bidirectional Encoder Representations from Transformers). For the experiment, classification is divided into two phases, namely the first phase groups emotion or not emotion tweets, and the second phase groups emotion tweets into five types of emotions, namely happy, angry, sad, scared and surprised. The experiment uses three data scenarios, namely the commuter line, transjakarta and combination data. The BERT method was compared with the BiLSTM and LSTM methods for emotion classification. The result of this study is BERT generates the highest accuracy compared to other methods for the three data scenarios with values of 87.24%, 86.27%, and 86.99% in the first phase. Then, the next accuracy value followed by obtained BiLSTM, and the last is LSTM. In the second phase, BERT also obtained the highest accuracy values for the three data scenarios with values of 94.26%, 93.71 %, and 93.98%. Then, the evaluation using Precision, Recall and F1-Measure results that BERT provides the best overall method. In this study, BERT has good performance for emotion classification.