Parsing Method Research Articles

Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning

With the development of technology, the popularity of online medical treatment is becoming more and more widespread. However, the accuracy and credibility of online medical treatment are affected by model design and semantic understanding. In particular, there are still some problems in the accurate understanding of complex structured texts, which affects the accuracy of judging users’ intentions and needs. Therefore, this paper proposes a new method for medical text parsing, which realizes core tasks such as named entity recognition, intention recognition, and slot filling through a multi-task learning framework; uses BERT to obtain contextual semantic information; and combines BiGRU and BiLSTM neural networks, and uses CRF to realize sequence annotation and DPCNN to realize classification prediction. Thus, the task of entity recognition and intention recognition can be accomplished. On this basis, this paper builds a multi-task learning model based on BiGRU-BiLSTM, and uses CBLUE and CMID databases to verify the method. The verification results show that the accuracy of named entity recognition and intention recognition reaches 86% and 89%, respectively, which improves the performance of various tasks. The ability of the model to process complex text is enhanced. This method can improve the text generalization ability and improve the accuracy of online medical intelligent dialogue when it is used to analyze medical texts.

Dynamic multi teacher knowledge distillation for semantic parsing in KBQA

Knowledge base question answering (KBQA) is an important task of extracting answers from a knowledge base by analyzing natural language questions. Semantic parsing methods convert natural language questions into executable logical forms to obtain answers on the knowledge base. Conventional approaches often prioritize singular logical forms, overlooking the distinct strengths inherent in various logical frameworks for problem solving. Recognizing that different logical forms may excel in addressing specific types of questions, our aim is to harness these strengths. By integrating the strengths of different logical forms, we expect to achieve more comprehensive and effective semantic parsing solutions. In our paper, we propose a Dynamic Multi Teacher Knowledge Distillation for Semantic Parsing (DMTKD-SP). DMTKD-SP leverages a collection of teacher models, each mastering a unique logical form, to collaboratively guide a student model so that knowledge from different logical forms can be transferred into the student model. To achieve this, we employ a confidence-based weight assignment module to dynamically assign weights for each teacher model. Furthermore, we introduce a self-distillation mechanism to mitigate the confusion caused by simultaneous learning from multiple teachers. We evaluate DMTKD-SP across variants of the KQA Pro dataset, demonstrating an accuracy improvement of 0.35% on five types of questions, with a notable 0.75% improvement for Count questions.

Application Of the National Corpus Of The Kazakh Language In Linguistic Research

National corpora are essential tools in modern linguistics, offering valuable data for analyzing language structures and their connection to the world.The primary goal of this study is to enhance the perception and analysis of the Kazakh language. It introduces a methodological approach that deepens the understanding of the structure and features of Kazakh.The research utilizes frequency analysis, syntactic parsing, and statistical methods to examine Kazakh language texts. The results demonstrate that the National Corpus provides extensive data for studying linguistic patterns. Frequency analysis identifies the most common words and phrases, while syntactic analysis uncovers patterns and connections between words. The study also highlights the variation in word usage and constructions, showing the language's adaptability to modern communication.The findings are applicable to lexicography, semantics, and syntax studies of the Kazakh language. Additionally, they offer practical benefits in language teaching, lexicographic resource creation, machine translation, and natural language processing.In conclusion, the research provides valuable tools for in-depth analysis of the Kazakh language, with broad applications across linguistic studies and practical fields.

Morphogenesis analysis of icosahedral and prismatic tensegrity modules

Morphogenesis is a critical aspect of tensegrity system design. This paper explores icosahedral and prismatic tensegrity modules' pattern formation and foundational theories. First, a material consumption functional and self-strain energy functional are introduced from an engineering perspective, along with a variational principle involving independent dual-variable in the morphogenesis process of tensegrity structures. Next, a Symbol Parsing Method is proposed and successfully applied to the optimal morphology analysis of the icosahedral tensegrity module. Due to memory limitations in personal computers, the Singular Value Decomposition of the symbolic equilibrium matrix for the prismatic tensegrity module is constrained. As an alternative, a discrete numerical solution, the semi-symbolic parsing method, is obtained by directly utilizing a numerical equilibrium matrix. This method was recently employed to analyze and optimize a practical tensegrity footbridge designed by the authors. Lastly, the paper delves into the stability of self-equilibrium states in icosahedral and classical prismatic tensegrity modules by employing a second-order variational analysis of the self-strain energy functional. The findings presented are expected to contribute to future research on the morphogenesis of tensegrity structures.

Analysis of Bi-LSTM CRF Series Models for Semantic Classification of NAVTEX Navigational Safety Messages

NAVTEX is a key component in the Global Maritime Distress and Safety System (GMDSS) that automatically transmits urgent maritime safety information such as navigational and meteorological warnings and forecasts to vessels. For the safe navigation of smart ships, this information from different systems should be shared harmoniously in the Common Maritime Data Structure (CMDS). To share NAVTEX messages as CMDS, words in NAVTEX messages must be semantically classified and placed within the CMDS structure. While traditional parsing methods are typically used to understand message semantics, NAVTEX requires natural language processing methods with deep learning due to its unstructured messages. This paper applies six types of Bi-LSTM CRF-based deep learning models to NAVTEX navigational safety messages and analyzes the results to find the most suitable model for understanding the semantics of each word in NAVTEX messages. This technique can be applied to accurately convey the meaning of NAVTEX navigational safety messages to equipment that requires navigational safety information on smart ships without human intervention.

From Simple to Complex Scenes: Learning Robust Feature Representations for Accurate Human Parsing.

Human parsing has attracted considerable research interest due to its broad potential applications in the computer vision community. In this paper, we explore several useful properties, including high-resolution representation, auxiliary guidance, and model robustness, which collectively contribute to a novel method for accurate human parsing in both simple and complex scenes. Starting from simple scenes: we propose the boundary-aware hybrid resolution network (BHRN), an advanced human parsing network. BHRN utilizes deconvolutional layers and multi-scale supervision to generate rich high-resolution representations. Additionally, it includes an edge perceiving branch designed to enhance the fineness of part boundaries. Building on BHRN, we construct a dual-task mutual learning (DTML) framework. It not only provides implicit guidance to assist the parser by incorporating boundary features, but also explicitly maintains the high-order consistency between the parsing prediction and the ground truth. Toward complex scenes: we develop a domain transform method to enhance the model robustness. By transforming the input space from the spatial domain to the polar harmonic Fourier moment domain, the mapping relationship to the output semantic space is highly stable. This transformation yields robust representations for both clean and corrupted data. When evaluated on standard benchmark datasets, our method achieves superior performance compared to state-of-the-art human parsing methods. Furthermore, our domain transform strategy significantly improves the robustness of DTML dramatically in most complex scenes.

Multi-Derivational Parsing of Vague Languages- The New Paradigm of Syntactic Pattern Recognition.

The new paradigm of syntactic pattern recognition, SPR, which uses multi-derivational parsing of vague languages is introduced in the paper. The methodology proposed addresses the issue of the recognition of vague/distorted patterns which is one of the important open problems in the area. The concept of the vague language of patterns and the efficient parsing method based on the class of dynamically programmed grammars are introduced. A vague language is defined with vague primitives which are vectors of "neighboring" primitives associated with measures of distance, probability, fuzziness, etc. The use of vague primitives allows us to identify b best structural templates during multi-derivational parsing that can be used for getting more adequate final result. The generic architecture of SPR system based on the approach proposed together with the system's applications for short-term electrical load forecasting and for analysis of ultrasound images in order to diagnose congenital defects of fetal palates are presented. The results of the experimental studies are discussed.

Sustainable urban road planning under the digital twin-MCDM-GIS framework considering multidisciplinary factors

Sustainable urban road planning necessitates the formulation of functional, economic, people-friendly, eco-friendly, and engineering-reasonable road schemes within the confines of limited urban space. It is challenging to meet traffic demands, stimulate economic growth, foster social progress, and minimize adverse impacts on the social and natural environment. This study proposes a comprehensive approach for sustainable urban road planning, employing the Digital Twin-MCDM-GIS framework. Digital twins play a pivotal role in converting diverse factors into comprehensible expressions. MCDM (multi-criteria decision making) evaluates and assigns weights to these multidisciplinary factors. GIS (geographic information system) offers an integrated digital space for analysis. Various digitalisation and parsing methods, including direct assignment methods, spatial analysis methods, and complex professional methods, are introduced for traffic, development, cost, social, environmental, and engineering factors. By assigning distinct weights to these factors, diverse raster data are established to generate multiple road schemes using the least-cost wide path analysis. To validate the proposed approach, a case study was conducted in the London Borough of Bromley, resulting in the generation of 14 road schemes with varying widths and factor considerations in limited urban space.

Matrix-based project dataset parsers

There are several existing project datasets, which involve separate data sources for simulated and real projects, individual and multiprojects, and single- and multimodal attributes. In addition, their file structures are heterogeneous; therefore, scholars can usually use only one dataset to test a proposed scheduling or resource allocation algorithm. Since the internal structures of these projects are also very different, it is difficult to ensure that an algorithm optimized for a given type of project will also perform well on projects with other structures. The proposed parsing method supports researchers in:•reading several types of projects: simulated, real, individual, and multiprojects, as well as single- and multimodal attributes;•considering the priorities of activities and the flexibility of their dependencies, which is essential for modeling the structural flexibility employed by agile, hybrid, and extreme project management approaches;•building a large project database for testing and comparing different scheduling and resource allocation algorithms.

DLLog: An Online Log Parsing Approach for Large-Scale System

Syslog is a critical data source for analyzing system problems. Converting unstructured log entries into structured log data is necessary for effective log analysis. However, existing log parsing methods demonstrate promising accuracy on limited datasets, but their generalizability and precision are uncertain when applied to diverse log data. Enhancements in these areas are necessary. This paper proposes an online log parsing method called DLLog, which is based on deep learning and has the longest common subsequence. DLLog utilizes the GRU neural network to mine template words and applies the longest common subsequence to parse log entries in real-time. In the offline stage, DLLog combines multiple log features to accurately extract the template words, creating a log template set to assist online log parsing. In the online stage, DLLog parses log entries by calculating the matching degree between the real-time log entry and the log template in the log template set. This method also supports the incremental update of the log template set to handle new log entries generated by systems. We summarized the previous works and validated DLLog using real log data collected from 16 systems. The results demonstrate that DLLog achieves high parsing accuracy, universality, and adaptability.

CoreNLP dependency parsing and pattern identification for enhanced opinion mining in aspect-based sentiment analysis

Aspect-Based Sentiment Analysis (ABSA) aims to identify the sentiment expressed towards a specific feature or aspect of a given text. Although certain ABSA techniques employ syntactic information to capture the connection between the opinion target and the sentiment word, they often do not incorporate data processing techniques such as dependency parsing, which can be beneficial in accurately capturing the sentiment expressed towards the opinion target. In this paper a method for ABSA that employs both syntax and semantic information and incorporates dependency parsing(Semantic-Syntactic Dependency Parsing (SSDP) Method) with Core Natural Language Processing (CoreNLP) which is a natural language processing library for processing the input text and identifying patterns effectively (according to the CoreNLP relations and part of speech tagging(POS)) to extract the critical relations that accurately reflect the sentiment conveyed regarding the opinion target is proposed. The results show that the proposed pattern captured approximately 75% of the data, and the rest were classified via Long Short-Term Memory (LSTM) based on semantic information. We illustrated the efficacy of SSDP, through experiments on the SemEval14,Semval15 and Semval16 datasets, which include two datasets (laptops and restaurants) carefully categorized by a human annotator into categories of positive, negative, or neutral attitudes. The experimental results reveal that SSDP is superior to the other state-of-the-art ABSA approaches, that use syntax information but do not utilize data processing techniques. Additionally, we highlight the limitations of ABSA methods that do not incorporate syntax information and the potential improvements that can be made through the use of data processing.

Semi-supervised log anomaly detection based on bidirectional temporal convolution network

System logs record system operation status and important event information. They are the important basis for debugging system failures and cause analysis. Due to the low accuracy of log parsing and insufficient labeled samples, anomaly detection precision is low. Therefore, we propose a new log-based semi-supervised anomaly detection method named BTCNLog. Firstly, the improved log parsing method with the dictionary keeps part of the parameter information in the log event. So, it can improve the utilization rate of log information and the accuracy of log parsing. Then, BERT is used to encode the semantic information to obtain the semantic vector of the log for the template. What's more, the clustering method is applied to estimate the tag to deal with insufficient data tagging problems. Therefore, it can improve the ability to detect unstable data for the model. Finally, a bidirectional temporal convolution network (Bi-TCN) with residual blocks is introduced to capture contextual information from two directions to improve the accuracy and efficiency of anomaly detection. To evaluate the performance of the proposed method, BTCNLog is compared with six baselines on two datasets. The final experimental results show that, compared with the latest three benchmark models, LogBERT, PLELog, and LogEncoder, the proposed method showed an average improvement of 7%, 14.1%, and 8.04% in F1 values.

Knowledge Graph-Based Reinforcement Federated Learning for Chinese Question and Answering

Knowledge question and answering (Q&A) is widely used. However, most existing semantic parsing methods in Q&A usually use cascading, which can incur error accumulation. In addition, using only one institution’s Q&A data definitely will limit the Q&A performance, while data privacy prevents sharing between institutions. This article proposes a knowledge graph-based reinforcement federated learning (KGRFL)-based Q&A approach to address these challenges. We design an end-to-end multitask semantic parsing model MSP-bidirectional and auto-regressive transformers (BART) that identifies question categories while converting questions into SPARQL statements to improve semantic parsing. Meanwhile, a reinforcement learning (RL)-based model fusion strategy is proposed to improve the effectiveness of federated learning, which enables multi-institution joint modeling and data privacy protection using cross-domain knowledge. In particular, it also reduces the negative impact of low-quality clients on the global model. Furthermore, a prompt learning-based entity disambiguation method is proposed to address the semantic ambiguity problem because of joint modeling. The experiments show that the proposed method performs well on different datasets. The Q&A results of the proposed approach outperform the approach of using only a single institution. Experiments also demonstrate that the proposed approach is resilient to security attacks, which is required for real applications.

Lightweight cross-guided contextual perceptive network for visible–infrared urban road scene parsing

Visible–infrared urban road scene parsing is attracting increasing attention because it can extract complementary cues from the visible and infrared imaging modalities. However, most existing parsing methods adopt complicated models, which incur large computational costs and limit real-time performance. Moreover, parsing methods may inadequately explore and apply high-level semantic information, considerably undermining the parsing accuracy. To solve these problems, we introduce a lightweight high-performance network called cross-guided contextual perceptive network (CCPNet). A lightweight backbone equipped with adaptive refined fusion modules reduces the size of CCPNet. Additionally, a cross-guided contextual perceptive module extracts and enhances semantic cues from high-level features. Experimental results indicate that CCPNet achieves state-of-the-art performance for visible–infrared scene parsing with few parameters (7.34 million), a small model (29.9 MB), and real-time inference (50.03 fps). The CCPNet code and results are available at: https://github.com/Jinfu0913/CCPNet.

Towards robust log parsing using self-supervised learning for system security analysis

Logs play an important role in anomaly detection, fault diagnosis, and trace checking of software and network systems. Log parsing, which converts each raw log line to a constant template and a variable parameter list, is a prerequisite for system security analysis. Traditional parsing methods utilizing specific rules can only parse logs of specific formats, and most parsing methods based on deep learning require labels. However, the existing parsing methods are not applicable to logs of inconsistent formats and insufficient labels. To address these issues, we propose a robust Log parsing method based on Self-supervised Learning (LogSL), which can extract templates from logs of different formats. The essential idea of LogSL is modeling log parsing as a multi-token prediction task, which makes the multi-token prediction model learn the distribution of tokens belonging to the template in raw log lines by self-supervision mode. Furthermore, to accurately predict the tokens of the template without labeled data, we construct a Multi-token Prediction Model (MPM) combining the pre-trained XLNet module, the n-layer stacked Long Short-Term Memory Net module, and the Self-attention module. We validate LogSL on 12 benchmark log datasets, resulting in the average parsing accuracy of our parser being 3.9% higher than that of the best baseline method. Experimental results show that LogSL has superiority in terms of robustness and accuracy. In addition, a case study of anomaly detection is conducted to demonstrate the support of the proposed MPM to system security tasks based on logs.

CPI-Parser: Integrating Causal Properties Into Multiple Human Parsing.

Existing methods of multiple human parsing (MHP) apply deep models to learn instance-level representations for segmenting each person into non-overlapped body parts. However, learned representations often contain many spurious correlations that degrade model generalization, leading learned models to be vulnerable to visually contextual variations in images (e.g., unseen image styles/external interventions). To tackle this, we present a causal property integrated parsing model termed CPI-Parser, which is driven by fundamental causal principles involving two causal properties for human parsing (i.e., the causal diversity and the causal invariance). Specifically, we assume that an image is constructed by a mix of causal factors (the characteristics of body parts) and non-causal factors (external contexts), where only the former ones decide the essence of human parsing. Since causal/non-causal factors are unobservable, the proposed CPI-Parser is required to separate key factors that satisfy the causal properties from an image. In this way, the parser is able to rely on causal factors w.r.t relevant evidence rather than non-causal factors w.r.t spurious correlations, thus alleviating model degradation and yielding improved parsing ability. Notably, the CPI-Parser is designed in a flexible way and can be integrated into any existing MHP frameworks. Extensive experiments conducted on three widely used benchmarks demonstrate the effectiveness and generalizability of our method. Code and models are released (https://github.com/HAG-uestc/CPI-Parser) for research purpose.

A Study of English Rhetorical Strategies Based on Neurosemantics

Abstract In recent years, with the performance improvement of deep neural network models, neural semantic parsing has entered a new stage and has been widely used in many fields such as classification tasks, speech recognition, linguistic annotation and syntactic analysis. In this paper, we design a strategy for recognizing and parsing English rhetorical devices using the neural semantic parsing method. In this paper, we first use the Skip-gram model to train word vectors, and then based on the convolutional neural network model to recognize the platitudes, metaphors, and similes in English text, and apply BiLSTM-Att model to acknowledge the English quoted rhetorics. The prediction accuracies of the CNN recognition model on platitudes, metaphors, and similes are 91.7%, 92.5%, and 90.2%, respectively, greater than 90%, indicating that the model can successfully recognize the platitudes, metaphors, and similes. The model can recognize platitudes, metaphors, and anthropomorphic rhetorical devices. Compared with the CNN+BiLSTM model, the precision, recall, and F1 value of the BiLSTM-Att model are improved by 0.22%, 11.44%, and 7.09%, respectively. Its recognition accuracy of English quotations under different similarity thresholds is very high, with 94% when the similarity reaches 50% and even 98% when the similarity reaches 90%. This suggests that the present paper BiLSTM-Att English quote rhetorical recognition model is adequate. This paper’s neural semantic-based English rhetoric strategy is paving the way for the development of natural language processing technology.

A Comparative Study of ChatGPT-based and Hybrid Parser-based Sentence Parsing Methods for Semantic Graph-based Induction

A Comparative Study of ChatGPT-based and Hybrid Parser-based Sentence Parsing Methods for Semantic Graph-based Induction

PosParser: A heuristic online log parsing method based on part-of-speech tagging

PosParser: A heuristic online log parsing method based on part-of-speech tagging

Vectorizing planar roof structure from very high resolution remote sensing images using transformers

ABSTRACT Accurately predicting the geometric structure of a building's roof as a vectorized representation from a raster image is a challenging task in building reconstruction. In this paper, we propose an efficient and precise parsing method called Roof-Former, based on a vision Transformer. Our method involves three steps: (1) Image encoder and edge node initialization, (2) Image feature fusion with an enhanced segmentation refinement branch, and (3) Edge filtering and structural reasoning. Our method outperforms previous works on the vectorizing world building dataset and the Enschede dataset, with vertex and edge heat map F1-scores increasing from 87.1 % , 76.2 % to 89.1 % , 78.1 % , and from 69.7 % , 68.8 % to 71.2 % , 69.5 % , respectively. Furthermore, our method demonstrates superior performance compared to the current state-of-the-art based on qualitative evaluations, indicating its effectiveness in extracting global image information while maintaining the consistency and topological validity of the roof structure.