Field Of NLP Research Articles

BERT-based log exception detection algorithm LADB

With the development of information systems, they have become increasingly large and complex and have generated a large amount of log information. These log information records the system's health, but the number of log information is huge, and the traditional exception detection algorithm in the case of very large amounts of data is difficult to efficiently and accurately detect anomalies due to poor generalization performance. A BERT-based log anomaly detection algorithm, LADB, is proposed, essentially a semi-supervised, multi-classification algorithm. (1) LADB uses the Transformer encoder as the base component for problems such as feature degradation and gradient explosion. (2) In order to make better use of the bidirectional context, and in view of BERT's excellence in the NLP field, the Masking Log Key Prediction (MLKP) self-monitoring task was designed, drawing on the idea of BERT's Masking Language Model. (3) In order to solve the problem of difficult and slow processing of high-dimensional data, the Deep SVDD algorithm is used for minimum superspheres capacity (VHM) self-supervision training task. Experiments have shown that LADB's combined performance is superior to the four representative log anomaly detection algorithms.

Deep learning approaches to lexical simplification: A survey

AbstractLexical Simplification (LS) is the task of substituting complex words within a sentence for simpler alternatives while maintaining the sentence’s original meaning. LS is the lexical component of Text Simplification (TS) systems with the aim of improving accessibility to various target populations such as individuals with low literacy or reading disabilities. Prior surveys have been published several years before the introduction of transformers, transformer-based large language models (LLMs), and prompt learning that have drastically changed the field of NLP. The high performance of these models has sparked renewed interest in LS. To reflect these recent advances, we present a comprehensive survey of papers published since 2017 on LS and its sub-tasks focusing on deep learning. Finally, we describe available benchmark datasets for the future development of LS systems.

A Natural Language Processing Model for Automated Organization and Analysis of Intangible Cultural Heritage

This paper investigates text similarity methods in the field of NLP, improves upon the WMD, and develops the SWC-WMD distance, forming the basis for a clustering method for long ICH texts. Clustering experiments on the constructed ICH long text dataset using WMD, SWC-WMD, and TF-IDF-WMD distances were conducted. The impact of the number of feature words on clustering results and the effect of different distances on clustering outcomes were assessed based on accuracy and F1 values from the evaluation criteria. The final results show that the SWC-WMD distance improves the accuracy and F1 values of the ICH long text clustering results compared to the other two distances, thereby proving the effectiveness of the methods proposed in this paper.

Natural Language Processing Patents Landscape Analysis

Understanding NLP patents provides valuable insights into innovation trends and competitive dynamics in artificial intelligence. This study uses the Lens patent database to investigate the landscape of NLP patents. The overall patent output in the NLP field on a global scale has exhibited a rapid growth over the past decade, indicating rising research and commercial interests in applying NLP techniques. By analyzing patent assignees, technology categories, and geographic distribution, we identify leading innovators as well as research hotspots in applying NLP. The patent landscape reflects intensifying competition between technology giants and research institutions. This research aims to synthesize key patterns and developments in NLP innovation revealed through patent data analysis, highlighting implications for firms and policymakers. A detailed understanding of NLP patenting activity can inform intellectual property strategy and technology investment decisions in this burgeoning AI domain.

TEXT GENERATION MODELS FOR PARAPHRASE ON KAZAKH LANGUAGE

This study delves into the relatively unexplored domain of natural language processing for the Kazakh language—a language with limited computational resources. The paper dissects the effectiveness of diffusion models and transformers in generating text, specifically paraphrases, which is a critical aspect of machine learning applications such as chatbots, virtual assistants, and automated translation services.
 The researchers methodically adapt these advanced models to understand and generate Kazakh text, tackling the unique challenges posed by the language's complex morphology. The paper is comprehensive in its approach, covering everything from the initial adaptation of the models to the Kazakh language context, to the creation of specialized tokenizer tools, to the translation and preparation of datasets for effective training.
 Through rigorous testing and performance analysis, the study identifies the strengths and weaknesses of each model type. This is critical as it informs the direction of future research and model development, with the goal of enhancing the fluency and accuracy of automated Kazakh text generation. The paper also discusses the broader impact of its findings, suggesting that the methodologies and insights gained could inform similar efforts in other low-resource languages, thereby contributing to the global field of NLP.
 The research concludes with reflections on the implications of their findings for the ongoing development of machine learning technologies, asserting the potential of these technologies to accommodate the intricacies of any language, given the right approach and resources. This work not only advances the technical capabilities for Kazakh text generation but also serves as a testament to the potential of machine learning to bridge language gaps and foster greater digital inclusivity.

Frozen CLIP Transformer Is an Efficient Point Cloud Encoder

The pretrain-finetune paradigm has achieved great success in NLP and 2D image fields because of the high-quality representation ability and transferability of their pretrained models. However, pretraining such a strong model is difficult in the 3D point cloud field due to the limited amount of point cloud sequences. This paper introduces Efficient Point Cloud Learning (EPCL), an effective and efficient point cloud learner for directly training high-quality point cloud models with a frozen CLIP transformer. Our EPCL connects the 2D and 3D modalities by semantically aligning the image features and point cloud features without paired 2D-3D data. Specifically, the input point cloud is divided into a series of local patches, which are converted to token embeddings by the designed point cloud tokenizer. These token embeddings are concatenated with a task token and fed into the frozen CLIP transformer to learn point cloud representation. The intuition is that the proposed point cloud tokenizer projects the input point cloud into a unified token space that is similar to the 2D images. Comprehensive experiments on 3D detection, semantic segmentation, classification and few-shot learning demonstrate that the CLIP transformer can serve as an efficient point cloud encoder and our method achieves promising performance on both indoor and outdoor benchmarks. In particular, performance gains brought by our EPCL are 19.7 AP50 on ScanNet V2 detection, 4.4 mIoU on S3DIS segmentation and 1.2 mIoU on SemanticKITTI segmentation compared to contemporary pretrained models. Code is available at \url{https://github.com/XiaoshuiHuang/EPCL}.

AaDR-PointCloud: An integrated point cloud processing network using attention and deep residual

3D point cloud has irregularity and disorder, which pose challenges for point cloud analysis. In the past, the projection or point cloud voxelization methods often used were insufficient in accuracy and speed. In recent years, the methods using Transformer in the NLP field or ResNet in the deep learning field have shown promising results. This article expands these ideas and introduces a novel approach. This paper designs a model AaDR-PointCloud that combines self-attention blocks and deep residual point blocks and operates iteratively to extract point cloud information. The self-attention blocks used in the model are particularly suitable for point cloud processing because of their order independence. The deep residual point blocks used provide the expression of depth features. The model performs point cloud classification and segmentation tests on two shape classification datasets and an object part segmentation dataset, achieving higher accuracy on these benchmarks.

Named Entity Recognition of Tunisian Arabic Using the Bi-LSTM-CRF Model

Named Entity Recognition (NER) is an NLP field that deals with recognizing and classifying entities in written text. Most Arabic NER research studies discuss the Arabic NER challenge for the Modern Standard Arabic (MSA) language. However, the presence of dialectal Arabic textual resources in social media, blogs, TV shows, etc. is increasingly progressive. Therefore, the treatment of named entities is rapidly becoming a necessity, particularly for dialectal Arabic. In this paper, we are interested in the collection and annotation of a corpus as well as the realization of a NER system for Tunisian Arabic (TA), named TUNER. To the best of the researchers’ knowledge, this is the first study that uses the suggested method for this purpose. In the present study, we adopt a hybrid method based on a Bi-LSTM-CRF model and a rule-based method. The proposed TUNER system yields an F-measure of 91.43%. This is an interesting improvement over comparable related work dialectal Arabic NER systems.

STTA: enhanced text classification via selective test-time augmentation.

Test-time augmentation (TTA) is a well-established technique that involves aggregating transformed examples of test inputs during the inference stage. The goal is to enhance model performance and reduce the uncertainty of predictions. Despite its advantages of not requiring additional training or hyperparameter tuning, and being applicable to any existing model, TTA is still in its early stages in the field of NLP. This is partly due to the difficulty of discerning the contribution of different transformed samples, which can negatively impact predictions. In order to address these issues, we propose Selective Test-Time Augmentation, called STTA, which aims to select the most beneficial transformed samples for aggregation by identifying reliable samples. Furthermore, we analyze and empirically verify why TTA is sensitive to some text data augmentation methods and reveal why some data augmentation methods lead to erroneous predictions. Through extensive experiments, we demonstrate that STTA is a simple and effective method that can produce promising results in various text classification tasks.

A Hybrid Deep Learning GRU based Approach for Text Classification using Word Embedding

Text categorization has become an increasingly important issue for businesses that handle massive volumes of data generated online, and it has found substantial use in the field of NLP. The capacity to group texts into separate categories is crucial for users to effectively retain and utilize important information. Our goal is to improve upon existing recurrent neural network (RNN) techniques for text classification by creating a deep learning strategy through our study. Raising the quality of the classifications made is the main difficulty in text classification, nevertheless, as the overall efficacy of text classification is often hampered by the data semantics' inadequate context sensitivity. Our study presents a unified approach to examine the effects of word embedding and the GRU on text classification to address this difficulty. In this study, we use the TREC standard dataset. RCNN has four convolution layers, four LSTM levels, and two GRU layers. RNN, on the other hand, has four GRU layers and four LSTM levels. One kind of recurrent neural network (RNN) that is well-known for its comprehension of sequential data is the gated recurrent unit (GRU). We found in our tests that words with comparable meanings are typically found near each other in embedding spaces. The trials' findings demonstrate that our hybrid GRU model is capable of efficiently picking up word usage patterns from the provided training set. Remember that the depth and breadth of the training data greatly influence the model's effectiveness. Our suggested method performs remarkably well when compared to other well-known recurrent algorithms such as RNN, MV-RNN, and LSTM on a single benchmark dataset. In comparison to the hybrid GRU's F-measure 0.952, the proposed model's F-measure is 0.982%. We compared the performance of the proposed method to that of the three most popular recurrent neural network designs at the moment RNNs, MV-RNNs, and LSTMs, and found that the new method achieved better results on two benchmark datasets, both in terms of accuracy and error rate.

STF-YOLO: A small target detection algorithm for UAV remote sensing images based on improved SwinTransformer and class weighted classification decoupling head

Due to the high altitude, high imaging resolution, complex background, and tilted shooting angle of the UAV images, detecting small targets poses challenges. In recent years, numerous deep learning-based methods have achieved good results in feature extraction and localization classification, but they fail to meet the needs of faster and more accurate recognition of small objects. This paper proposes a novel UAV remote sensing image small target detection algorithm called STF-YOLO to address the aforementioned issues. Firstly, we introduce the innovative SwinTransformer, a popular technique in the field of NLP, and combine it with CNNs to propose a novel convolutional structure called STRCN. Furthermore, this paper presents a novel lightweight classifier called CNeB, which is innovatively designed and incorporated into the Backbone part. CNeB exhibits advantages such as high recognition accuracy and parameter efficiency. During the process, we further enhance the prediction accuracy by incorporating a parameter-free attention mechanism called SimAM to improve the overall precision of the model. Moreover, we propose a novel detection head called CWDHead, which strengthens the weighting of classification capabilities and significantly improves the recognition accuracy of similar inter-class and intra-class instances. Lastly, we design a novel data augmentation method called SVM, which enriches the training dataset and enhances the robustness and generalization ability of the model. The results indicate that by applying enhancements such as WBF and TTA, our proposed algorithm, STF-YOLO, exhibits significant improvements over current state-of-the-art methods on the publicly available VisDrone dataset. It demonstrates noticeable advancements in widely recognized metrics such as 3.9%mAP, and 2.0%AP50.

Linguistic mastery: Advances in natural language processing

NLPstands for Natural Language Processing, it is a kind of artificial intelligence. That demonstrate with scrutinize, understanding, and accuse natural human languages. In such a manner that analog procedure would get in touch and human language excluding computer-propel language like programming language such as c, c++, python, javascript. NLP field contains the intellect computer programming into an understanding language which is understandable by humans. So, powerful algorithms can even interpret one language into another language scrupulously. Natural language processing occasionally also known as computational linguistics, it uses semantics and syntax. It assists computers to acknowledge how humans talk or write and also it will know how to deduce meaning of words that they are saying. A language is designate as a synchronize of rules and symbols. These Symbol are amalgamated and used for disseminate the data as well as make contact to other resources. Symbols are potentiate by the rules and regulations.

Q8VaxStance: Dataset Labeling System for Stance Detection towards Vaccines in Kuwaiti Dialect

The Kuwaiti dialect is a particular dialect of Arabic spoken in Kuwait; it differs significantly from standard Arabic and the dialects of neighboring countries in the same region. Few research papers with a focus on the Kuwaiti dialect have been published in the field of NLP. In this study, we created Kuwaiti dialect language resources using Q8VaxStance, a vaccine stance labeling system for a large dataset of tweets. This dataset fills this gap and provides a valuable resource for researchers studying vaccine hesitancy in Kuwait. Furthermore, it contributes to the Arabic natural language processing field by providing a dataset for developing and evaluating machine learning models for stance detection in the Kuwaiti dialect. The proposed vaccine stance labeling system combines the benefits of weak supervised learning and zero-shot learning; for this purpose, we implemented 52 experiments on 42,815 unlabeled tweets extracted between December 2020 and July 2022. The results of the experiments show that using keyword detection in conjunction with zero-shot model labeling functions is significantly better than using only keyword detection labeling functions or just zero-shot model labeling functions. Furthermore, for the total number of generated labels, the difference between using the Arabic language in both the labels and prompt or a mix of Arabic labels and an English prompt is statistically significant, indicating that it generates more labels than when using English in both the labels and prompt. The best accuracy achieved in our experiments in terms of the Macro-F1 values was found when using keyword and hashtag detection labeling functions in conjunction with zero-shot model labeling functions, specifically in experiments KHZSLF-EE4 and KHZSLF-EA1, with values of 0.83 and 0.83, respectively. Experiment KHZSLF-EE4 was able to label 42,270 tweets, while experiment KHZSLF-EA1 was able to label 42,764 tweets. Finally, the average value of annotation agreement between the generated labels and human labels ranges between 0.61 and 0.64, which is considered a good level of agreement.

Comparing Selective Masking Methods for Depression Detection in Social Media

Abstract Identifying those at risk for depression is a crucial issue and social media provides an excellent platform for examining the linguistic patterns of depressed individuals. A significant challenge in depression classification problems is ensuring that prediction models are not overly dependent on topic keywords (i.e., depression keywords) such that it fails to predict when such keywords are unavailable. One promising approach is masking—that is, by selectively masking various words and asking the model to predict the masked words, the model is forced to learn the inherent language patterns of depression. This study evaluates seven masking techniques. Moreover, predicting the masked words during the pre-training or fine-tuning phase was also examined. Last, six class imbalanced ratios were compared to determine the robustness of masked words selection methods. Key findings demonstrate that selective masking outperforms random masking in terms of F1-score. The most accurate and robust models are identified. Our research also indicates that reconstructing the masked words during the pre-training phase is more advantageous than during the fine-tuning phase. Further discussion and implications are discussed. This is the first study to comprehensively compare masked words selection methods, which has broad implications for the field of depression classification and general NLP. Our code can be found at: https://github.com/chanapapan/Depression-Detection.

DAQAS: Deep Arabic Question Answering System based on duplicate question detection and machine reading comprehension

As of late, various deep learning techniques and methods have shown their superiority to feature-based and shallow learning techniques in the field of open-domain question–answering systems (OpenQAS). However, only a few works adopted these techniques to build Arabic OpenQAS that can extract exact answers from large information sources (e.g., Wikipedia). In addition, no available Arabic OpenQAS integrated a module to identify duplicate questions to accelerate response time and reduce computation cost. In this paper, we propose an Arabic OpenQAS (named DAQAS) based on deep learning methods. It consists of three components: (1) Dense Duplicate Question Detection which returns answers to questions that already have been answered; (2) Retriever based on BM25 and Query Expansion by neural text generation; and (3) Reader able to extract exact answers given a question and the retrieved passages that probably contains the answer. All components of our system integrate deep learning models, specially transformers-based techniques, which have scored state-of-the-art in different NLP fields. We performed several experiments with publicly available question answering datasets to show the effectiveness of our system. DAQAS obtained promising results and scored 21.77% Exact Match and 54.71% F1 score when using only top 5 retrieved passages.

MeT: A graph transformer for semantic segmentation of 3D meshes

Polygonal meshes have become the standard for discretely approximating 3D shapes, thanks to their efficiency and high flexibility in capturing non-uniform shapes. This non-uniformity, however, leads to irregularity in the mesh structure, making tasks like segmentation of 3D meshes particularly challenging. Semantic segmentation of 3D mesh has been typically addressed through CNN-based approaches, leading to good accuracy. Recently, transformers have gained enough momentum both in NLP and computer vision fields, achieving performance at least on par with CNN models, supporting the long-sought architecture universalism. Following this trend, we propose a transformer-based method for semantic segmentation of 3D mesh motivated by a better modeling of the graph structure of meshes, by means of global attention mechanisms. In order to address the limitations of standard transformer architectures in modeling relative positions of non-sequential data, as in the case of 3D meshes, as well as in capturing the local context, we perform positional encoding by means the Laplacian eigenvectors of the adjacency matrix, replacing the traditional sinusoidal positional encodings, and by introducing clustering-based features into the self-attention and cross-attention operators. Experimental results, carried out on three sets of the Shape COSEG Dataset (Wang et al., 2012), on the human segmentation dataset proposed in Maron et al. (2017) and on the ShapeNet benchmark (Chang et al., 2015), show how the proposed approach yields state-of-the-art performance on semantic segmentation of 3D meshes.

HRDoc: Dataset and Baseline Method toward Hierarchical Reconstruction of Document Structures

The problem of document structure reconstruction refers to converting digital or scanned documents into corresponding semantic structures. Most existing works mainly focus on splitting the boundary of each element in a single document page, neglecting the reconstruction of semantic structure in multi-page documents. This paper introduces hierarchical reconstruction of document structures as a novel task suitable for NLP and CV fields. To better evaluate the system performance on the new task, we built a large-scale dataset named HRDoc, which consists of 2,500 multi-page documents with nearly 2 million semantic units. Every document in HRDoc has line-level annotations including categories and relations obtained from rule-based extractors and human annotators. Moreover, we proposed an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem. By adopting a multi-modal bidirectional encoder and a structure-aware GRU decoder with soft-mask operation, the DSPS model surpass the baseline method by a large margin. All scripts and datasets will be made publicly available at https://github.com/jfma-USTC/HRDoc.

Research on Sentiment Analysis of Government Short Video Comments based on BERT's Multi Strategy Combination Model

In the field of NLP, pre trained models greatly shorten development time, reduce usage difficulty, and improve model robustness. In this paper, the BERT pre training model is used to analyze the text emotion of Tiktok short video comments. Through experimental comparison, it is found that the BERT pre training model is better than CNN and BiLSTM in emotion classification. Based on the BERT model, the multi strategy hybrid method is used to further input the BERT output dynamic word vector into the CNN and BiLSTM models to further extract local features and features with long distance dependency, improve the accuracy of the model, and obtain a good classification effect on the government short video comment dataset, which is better than the single model.

A Parallel Fusion Graph Convolutional Network for Aspect-Level Sentiment Analysis

Sentiment analysis has always been an important basic task in the NLP field. Recently, graph convolutional networks (GCNs) have been widely used in aspect-level sentiment analysis. Because GCNs have good aggregation effects, every node can contain neighboring node information. However, in previous studies, most models used only a single GCN to learn contextual information. The GCN relies on the construction method of the graph, and a single GCN will cause the model to focus on a certain relationship of nodes that depends on the construction method and ignore other information. In addition, when the GCN aggregates node information, it cannot determine whether the aggregated information is useful, so it will inevitably introduce noise. We propose a model that fuses two parallel GCNs to learn different relational features between sentences at the same time, and we add a gate mechanism to the GCN to filter the noise introduced by the GCN when aggregating information. Finally, we validate our model on public datasets, and the experiments show that compared to state-of-the-art models, our model performs better.

Emotion Detection on Social Media

Abstract: We proposed combine approach of BiLSTM and CNN to capture the emotional information from the words we are going to train BiLSTM model to get the semantic information from the words and parallelly we will be training CNN to get emotional information from the words. We will be using FastText word vector method for obtaining vectors from words unlike Word2Vec it operates at more minute level with character n grams. CNN and RNN are more efficient in finding the patterns out of large dataset. Due to the Word2Vec it opened entirely new approach to NLP. LSTM algorithms are quite popular in NLP field there were used in many emotion detection model due to their ability to find semantic meaning of words but they fail to capture information about the words. CNN models are quite fascinating and due to their ability to find the complex pattern among the images they are getting lot of attention in NLP. But, they are subjected to many limitations as network is designed to handle single dimension so it became vague how they are going to generalizes multi class prediction.