Performance Enhancement of XML Parsing Using Regression and Parallelism

The Extensible Markup Language(XML)files,widely used for storing and exchanging information on the web require efficient parsing mechanisms to improve the performance of the applications.With the existing Document Object Model(DOM)based parsing,the performance degrades due to sequential processing and large memory requirements,thereby requiring an efficient XML parser to mitigate these issues.In this paper,we propose a Parallel XML Tree Generator(PXTG)algorithm for accelerating the parsing of XML files and a Regression-based XML Parsing Framework(RXPF)that analyzes and predicts performance through profiling,regression,and code generation for efficient parsing.The PXTG algorithm is based on dividing the XML file into n parts and producing n trees in parallel.The profiling phase of the RXPF framework produces a dataset by measuring the performance of various parsing models including StAX,SAX,DOM,JDOM,and PXTG on different cores by using multiple file sizes.The regression phase produces the prediction model,based on which the final code for efficient parsing of XML files is produced through the code generation phase.The RXPF framework has shown a significant improvement in performance varying from 9.54%to 32.34%over other existing models used for parsing XML files.

Optimization of Mobile Network Radio Coverage by Automating Radio Parameter Updates Using Parsing

The present work aims is to propose a solution for automating updates (MAJ) of the radio parameters of the ATOLL database from the OSS NetAct using Parsing. Indeed, this solution will be operated by the RAN (Radio Access Network) service of mobile operators, which ensures the planning and optimization of network coverage. The overall objective of this study is to make synchronous physical data of the sites deployed in the field with the ATOLL database which contains all the data of the coverage of the mobile networks of the operators. We have made an application that automates, updates with the following functionalities: import of radio parameters with the parsing method we have defined, visualization of data and its export to the Template of the ATOLL database. The results of the tests and validations of our application developed for a 4G network have made it possible to have a solution that performs updates with a constraint on the size of data to be imported. Our solution is a reliable resource for updating the databases containing the radio parameters of the network at all mobile operators, subject to a limitation in terms of the volume of data to be imported.

Clothing Parsing Based on Multi-Scale Fusion and Improved Self-Attention Mechanism

Due to the lack of long-range association and spatial location information,fine details and accurate boundaries of complex clothing images cannot always be obtained by using the existing deep learning-based methods.This paper presents a convolutional structure with multi-scale fusion to optimize the step of clothing feature extraction and a self-attention module to capture long-range association information.The structure enables the self-attention mechanism to directly participate in the process of information exchange through the down-scaling projection operation of the multi-scale framework.In addition,the improved self-attention module introduces the extraction of 2-dimensional relative position information to make up for its lack of ability to extract spatial position features from clothing images.The experimental results based on the colorful fashion parsing dataset(CFPD)show that the proposed network structure achieves 53.68%mean intersection over union(mIoU)and has better performance on the clothing parsing task.

Improved head-driven statistical models for natural language parsing

Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, "valency" is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling(SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.

Chunk Parsing and Entity Relation Extracting to Chinese Text by Using Conditional Random Fields Model

Currently, large amounts of information exist in Web sites and various digital media. Most of them are in natural lan-guage. They are easy to be browsed, but difficult to be understood by computer. Chunk parsing and entity relation extracting is important work to understanding information semantic in natural language processing. Chunk analysis is a shallow parsing method, and entity relation extraction is used in establishing relationship between entities. Because full syntax parsing is complexity in Chinese text understanding, many researchers is more interesting in chunk analysis and relation extraction. Conditional random fields (CRFs) model is the valid probabilistic model to segment and label sequence data. This paper models chunk and entity relation problems in Chinese text. By transforming them into label solution we can use CRFs to realize the chunk analysis and entities relation extraction.

Residual Network with Enhanced Positional Attention and Global Prior for Clothing Parsing

Clothing parsing, also known as clothing image segmentation, is the problem of assigning a clothing category label to each pixel in clothing images. To address the lack of positional and global prior in existing clothing parsing algorithms, this paper proposes an enhanced positional attention module(EPAM) to collect positional information in the vertical direction of each pixel, and an efficient global prior module(GPM) to aggregate contextual information from different sub-regions. The EPAM and GPM based residual network(EG-ResNet) could effectively exploit the intrinsic features of clothing images while capturing information between different scales and sub-regions. Experimental results show that the proposed EG-ResNet achieves promising performance in clothing parsing of the colorful fashion parsing dataset(CFPD)(51.12% of mean Intersection over Union(mIoU) and 92.79% of pixel-wise accuracy(PA)) compared with other state-of-the-art methods.

Two-stage approach to full Chinese parsing

Natural language parsing is a task of great importance and extreme difficulty. In this paper, we present a full Chinese parsing system based on a two-stage approach. Rather than identifying all phrases by a uniform model, we utilize a divide and conquer strategy. We propose an effective and fast method based on Markov model to identify the base phrases. Then we make the first attempt to extend one of the best English parsing models i.e. the head-driven model to recognize Chinese complex phrases. Our two-stage approach is superior to the uniform approach in two aspects. First, it creates synergy between the Markov model and the head-driven model. Second, it reduces the complexity of full Chinese parsing and makes the parsing system space and time efficient. We evaluate our approach in PARSEVAL measures on the open test set, the parsing system performances at 87.53% precision, 87.95% recall.

SUBDIVIDING VERBS TO IMPROVE SYNTACTIC PARSING

This paper proposes a new way to improve the performance of dependency parser: subdividing verbs according to their grammatical functions and integrating the information of verb subclasses into lexicalized parsing model. Firstly,the scheme of verb subdivision is described. Secondly,a maximum entropy model is presented to distinguish verb subclasses. Finally,a statistical parser is developed to evaluate the verb subdivision. Experimental results indicate that the use of verb subclasses has a good influence on parsing performance.

Design and Implementation of Weibo Sentiment Analysis Based on LDA and Dependency Parsing

Information content security is a branch of cyberspace security. How to effectively manage and use Weibo comment information has become a research focus in the field of information content security. Three main tasks involved are emotion sentence identification and classification,emotion tendency classification,and emotion expression extraction. Combining with the latent Dirichlet allocation(LDA) model,a Gibbs sampling implementation for inference of our algorithm is presented,and can be used to categorize emotion tendency automatically with the computer. In accordance with the lower ratio of recall for emotion expression extraction in Weibo,use dependency parsing,divided into two categories with subject and object,summarized six kinds of dependency models from evaluating objects and emotion words,and proposed that a merge algorithm for evaluating objects can be accurately evaluated by participating in a public bakeoff and in the shared tasks among the best methods in the sub-task of emotion expression extraction,indicating the value of our method as not only innovative but practical.

Fast Chinese syntactic parsing method based on conditional random fields

A fast method for phrase structure grammar analysis is proposed based on conditional ran- dom fields （CRF）. The method trains several CRF classifiers for recognizing the phrase nodes at dif- ferent levels, and uses the bottom-up to connect the recognized phrase nodes to construct the syn- tactic tree. On the basis of Beijing forest studio Chinese tagged corpus, two experiments are de- signed to select the training parameters and verify the validity of the method. The result shows that the method costs 78. 98 ms and 4. 63 ms to train and test a Chinese sentence of 17. 9 words. The method is a new way to parse the phrase structure grammar for Chinese, and has good generalization ability and fast speed.

Video events recognition by improved stochastic parsing based on extended stochastic context-free grammar representation

Video events recognition is a challenging task for high-level understanding of video se- quence. At present, there are two major limitations in existing methods for events recognition. One is that no algorithms are available to recognize events which happen alternately. The other is that the temporal relationship between atomic actions is not fully utilized. Aiming at these problems, an algo- rithm based on an extended stochastic context-free grammar （SCFG） representation is proposed for events recognition. Events are modeled by a series of atomic actions and represented by an extended SCFG. The extended SCFG can express the hierarchical structure of the events and the temporal re- lationship between the atomic actions. In comparison with previous work, the main contributions of this paper are as follows： ① Events （include alternating events） can be recognized by an improved stochastic parsing and shortest path finding algorithm. ② The algorithm can disambiguate the detec- tion results of atomic actions by event context. Experimental results show that the proposed algo- rithm can recognize events accurately and most atomic action detection errors can be corrected sim- ultaneously.

Analysis of postoperative ocular surface changes and intervention effect after pars plana vitrectomy in meibomian gland dysfunction dry eye patients

AIM:To observe ocular surface changes after phacovitrectomy in patients with mild to moderate meibomian gland dysfunction(MGD)-type dry eye and track clinical treatment response using a Keratograph 5M and a Lipi View interferometer.METHODS:Forty cases were randomized into control group A and treatment group B;the latter received meibomian gland treatment 3d before phacovitrectomy and sodium hyaluronate before and after surgery.The average non-invasive tear film break-up time(NITBUTav),first noninvasive tear film break-up time(NITBUTf),non-invasive measured tear meniscus height(NTMH),meibomian gland loss(MGL),lipid layer thickness(LLT)and partial blink rate(PBR)were measured preoperatively and 1wk,1 and 3mo postoperatively.RESULTS:The NITBUTav values of group A at 1wk(4.38±0.47),1mo(6.76±0.70),and 3mo(7.25±0.68)were significantly lower than those of group B(7.45±0.78,10.46±0.97,and 11.31±0.89;P=0.002,0.004,and 0.001,respectively).The NTMH values of group B at 1wk(0.20±0.01)and 1mo(0.22±0.01)were markedly higher than those of group A(0.15±0.01 and 0.15±0.01;P=0.008 and P<0.001,respectively);however,there was no difference at 3mo.The LLT of group B at 3mo[91.5(76.25-100.00)]significantly exceeded that of group A[65.00(54.50-91.25),P=0.017].No obvious intergroup difference was found in MGL or PBR(P>0.05).CONCLUSION:Mild to moderate MGD dry eye worsens in the short term after phacovitrectomy.Preoperative cleaning,hot compresses,and meibomian gland massage as well as preoperative and postoperative sodium hyaluronate promote the rapid recovery of tear film stability.

A Modular Incremental Model for English Full Parsing

In this paper, we present a modular incremental statistical model for English full parsing. Unlike other full parsing approaches in which the analysis of the sentence is a uniform process, our model separates the full parsing into shallow parsing and sentence skeleton parsing. In shallow parsing, we finish POS tagging, Base NP identification, prepositional phrase attachment and subordinate clause identification. In skeleton parsing, we use a layered feature-oriented statistical method. Modularity possesses the advantage of solving different problems in parsing with corresponding mechanisms. Feature-oriented rule is able to express the complex lingual phenomena at the key point if needed. Evaluated on Penn Treebank corpus, we obtained 89.2% precision and 89.8% recall.

An Efficient Way to Parse Logs Automatically for Multiline Events

In order to obtain information or discover knowledge from system logs,the first step is to performlog parsing,whereby unstructured raw logs can be transformed into a sequence of structured events.Although comprehensive studies on log parsing have been conducted in recent years,most assume that one event object corresponds to a single-line message.However,in a growing number of scenarios,one event object spans multiple lines in the log,for which parsing methods toward single-line events are not applicable.In order to address this problem,this paper proposes an automated log parsing method for multiline events(LPME).LPME finds multiline event objects via iterative scanning,driven by a set of heuristic rules derived from practice.The advantage of LPME is that it proposes a cohesion-based evaluation method for multiline events and a bottom-up search approach that eliminates the process of enumerating all combinations.We analyze the algorithmic complexity of LPME and validate it on four datasets from different backgrounds.Evaluations show that the actual time complexity of LPME parsing for multiline events is close to the constant time,which enables it to handle large-scale sample inputs.On the experimental datasets,the performance of LPME achieves 1.0 for recall,and the precision is generally higher than 0.9,which demonstrates the effectiveness of the proposed LPME.

融合情感语义与句法结构的中文开放域事理图谱构建研究

为解决大规模开放域事理图谱构建过程中缺少标注数据以及事件类型未知导致的限定域事理图谱构建方法难以迁移的问题,利用规则匹配方法高效识别开放域文本中包含的多种事件逻辑关系,融合情感语义与句法结构信息分析提高事件抽取准确性,以更好完成事理图谱的构建任务。首先,总结并扩展因果、顺承、条件、转折等多种逻辑关系抽取模板,并基于规则模板、依存句法信息筛选逻辑关系事件句;其次,创新性地引入情感语义分析方法,在句法结构信息的基础上,通过捕获事件及事件间关系的情感语义精准识别事件类型,进而抽取事件论元;再次,计算语义相似度,进行事件融合,构建<前序事件,事件逻辑关系,后序事件>三元组,得到事件事理图谱,并进一步进行事件泛化以构建抽象事理图谱;最后,以事件发展较完整的“2022年猴痘事件”为数据源,通过实证分析证明开放域事理图谱构建方法可以实现不同类型事件的识别、事件间逻辑关系的揭露,其有效性、可行性得到验证。研究不仅弥补了现有事理图谱构建理论的不足,也为决策支持、事件发展预测等提供有力的数据支持。

WT-Parser:一种有效的在线日志解析方法

系统日志是用于记录系统运行情况的重要数据,通过分析日志可以有效监测系统运行状态、检测系统异常。随着大数据,云计算等技术的兴起,系统日志随着系统规模的扩大也不断增加,如何实现日志自动化分析成为当前研究热点,将非结构化日志文本解析为结构化数据是日志分析的基础。针对大多数日志解析方法难以有效分离日志状态变量的问题,文章提出在线日志解析方法(WT-Parser),方法通过日志单词构成来筛选模板单词用于日志聚类,并采用前缀树的思想提取日志模板。实验使用五个真实的日志数据集对方法进行评估,结果表明,WT-Parser能有效区分日志的状态变量,并且在大多数数据集上都显示了更加优异的性能。

ASE-ERNIE:一种基于ERNIE的中文指令动作序列生成方法

中文结构灵活多样,同样的语义有多种表达方式,在中文指令过长或存在多组动作的情况下,家庭服务机器人很难正确理解并做出响应。此外,家庭环境的中文指令解析研究在数据方面仍然稀缺。现有的方法不能准确提取出指令中包含的全部动作序列。由此提出一种基于ERNIE方法的动作序列提取(ASE-ERNIE)模型来提取中文指令中的可执行动作序列,该模型通过动作类型和参数之间的关系提取指令信息,将动作序列的提取工作转化为序列标注任务。在指令中存在多组动作并且存在多个参数的情况下,依然能够提取出全部动作序列。通过实验验证了所提方法的有效性,动作序列提取任务的F 1值达到了80.37%。

辅助任务增强的中文跨域NL2SQL算法

自然语言到结构化查询语言(natural language to structured query language,NL2SQL)任务旨在将自然语言询问转化为数据库可执行的结构化查询语言(structured query language,SQL)语句。本文提出了一种辅助任务增强的中文跨域NL2SQL算法,其核心思想是通过在解码阶段添加辅助任务以结合原始模型来进行多任务训练,提升模型的准确率。辅助任务的设计是通过将数据库模式建模成图,预测自然语言询问与数据库模式图中的节点的依赖关系,显式地建模自然语言询问和数据库模式之间的依赖关系。针对特定的自然语言询问,通过辅助任务的提升,模型能够更好地识别数据库模式中哪些表/列对预测目标SQL更有效。在中文NL2SQL数据集DuSQL上的实验结果表明,添加辅助任务后的算法相对于原始模型取得了更好的效果,能够更好地处理跨域NL2SQL任务。

基于语篇解析和图注意力网络的对话情绪识别

对话情绪识别研究主要聚焦于融合对话上下文和说话者建模的相互关系。当前研究通常忽略对话内部存在的依存关系,导致对话的上下文联系不够紧密,说话者之间的关系也缺乏逻辑。因此,提出了一种基于语篇解析和图注意力网络(discourse parsing and graph attention network,DPGAT)的对话情绪识别模型,将对话内部的依存关系融入语境建模过程中,使语境信息更具有依赖性和全局性。首先,通过语篇解析获取对话内部的话语依存关系,构建语篇依存关系图和说话者关系图。随后,通过多头注意力机制将不同类型的说话者关系图进行内部融合。此外,在图注意力网络的基础上,结合依存关系进行循环学习,以达到上下文信息和说话人信息的有效融合,实现对话语境信息的外部融合。最终,通过分析内、外部融合的结果还原完整对话语境,并对说话者的情绪进行分析。通过在英文数据集MELD、EmoryNLP、DailyDialog和中文数据集M3ED上进行评估验证,F1分数分别为66.23%、40.03%、59.28%、52.77%,与主流的模型相比,所提模型具有较好的适用性,可在不同的语言场景中使用。