Construction method of Chinese sentential semantic structure

A new method is proposed for constructing the Chinese sentential semantic structure in this paper. The method adopts the features including predicates, relations between predicates and basic arguments, relations between words, and case types to train the models of CRF ＋ ＋ and de- pendency parser. On the basis of the data set in Beijing Forest Studio-Chinese Tagged Corpus （ BFS- CTC）, the proposed method obtains precision value of 73.63% in open test. This result shows that the formalized computer processing can construct the sentential semantic structure absolutely. The features of predicates, topic and comment extracted with the method can be applied in Chinese in- formation processing directly for promoting the development of Chinese semantic analysis. The method makes the analysis of sentential semantic analysis based on large scale of data possible. It is a tool for expanding the corpus and has certain theoretical research and practical application value.

Enhancing Navigability:An Algorithm for Constructing Tag Trees

Purpose： This study introduces an algorithm to construct tag trees that can be used as a userfriendly navigation tool for knowledge sharing and retrieval by solving two issues of previous studies, i.e. semantic drift and structural skew.Design/methodology/approach： Inspired by the generality based methods, this study builds tag trees from a co-occurrence tag network and uses the h-degree as a node generality metric. The proposed algorithm is characterized by the following four features：（1） the ancestors should be more representative than the descendants,（2） the semantic meaning along the ancestor-descendant paths needs to be coherent,（3） the children of one parent are collectively exhaustive and mutually exclusive in describing their parent, and（4） tags are roughly evenly distributed to their upper-level parents to avoid structural skew. Findings： The proposed algorithm has been compared with a well-established solution Heymann Tag Tree（HTT）. The experimental results using a social tag dataset showed that the proposed algorithm with its default condition outperformed HTT in precision based on Open Directory Project（ODP） classification. It has been verified that h-degree can be applied as a better node generality metric compared with degree centrality.Research limitations： A thorough investigation into the evaluation methodology is needed, including user studies and a set of metrics for evaluating semantic coherence and navigation performance.Practical implications： The algorithm will benefit the use of digital resources by generating a flexible domain knowledge structure that is easy to navigate. It could be used to manage multiple resource collections even without social annotations since tags can be keywords created by authors or experts, as well as automatically extracted from text.Originality/value： Few previous studies paid attention to the issue of whether the tagging systems are easy to navigate for users. The contributions of this study are twofold：（1） an algorithm was developed to construct tag trees with consideration given to both semanticcoherence and structural balance and（2） the effectiveness of a node generality metric, h-degree, was investigated in a tag co-occurrence network.

HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

Spreadsheets contain a lot of valuable data and have many practical applications.The key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell pairs.Most existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell pairs.In this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of spreadsheets.For improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location information.Our experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

Research on Metamodels Consistency Verification Based on Formalization of Domain-Specific Metamodeling Language

Domain-specific metamodeling language(DSMML) defined by informal method cannot strictly represent its structural semantics,so its properties such as consistency cannot be holistically and systematically verified.In response,the paper proposes a formal representation of the structural semantics of DSMML named extensible markup language(XML) based metamodeling language(XMML) and its metamodels consistency verification method.Firstly,we describe our approach of formalization,based on this,the method of consistency verification of XMML and its metamodels based on first-order logical inference is presented;then,the formalization automatic mapping engine for metamodels is designed to show the feasibility of our formal method.

Direct Model Checking Matrix Algorithm

During the last decade, Model Checking has proven its efficacy and power in circuit design, network protocol analysis and bug hunting. Recent research on automatic verification has shown that no single model-checking technique has the edge over all others in all application areas. So, it is very difficult to determine which technique is the most suitable for a given model. It is thus sensible to apply different techniques to the same model. However, this is a very tedious and time-consuming task, for each algorithm uses its own description language. Applying Model Checking in software design and verification has been proved very difficult. Software architectures （SA） are engineering artifacts that provide high-level and abstract descriptions of complex software systems. In this paper a Direct Model Checking （DMC） method based on Kripke Structure and Matrix Algorithm is provided. Combined and integrated with domain specific software architecture description languages （ADLs）, DMC can be used for computing consistency and other critical properties.

Study on semantic structure of cultural and creative industry park based on internet literature

According to the information of 231 cultural and creative industry parks on the Internet literature, this research classified and studied the park elements from the text using the method of semantic Ontotogy analysis. The paper analyzes the cultural and creative industry park in the performance of type, constitution elements of geosraphical attributes, character- istics, etc. Through analysis, we found that ＂location＂, ＂form＂ and ＂rhetoric＂ are frequently mentioned in the park names. As the parks prefer to combine cultural relics with their own characters for hishlighting influence and specialties, the park locations also have certain restrictions （e.g., ＂historic sites＂, ＂ancient cities＂）. Furthermore, cultural and creative industry parks cover a wide range of contents （e.g., ＂digital industry＂, ＂ecological landscape＂）. The boundaries of the park type and content are not very clear in present China. And, the parks lack of overall planning and often have an imperfect industrial chain and other issues. Therefore, on developing cultural and creative industry parks, the overall planning and perfect industrial chain need to be focused on.

Chinese Semantic Parsing Based on Feature Structure with Recursive Directed Graph

It is difficult to analyze semantic relations automatically, especially the semantic relations of Chinese special sentence patterns. In this paper, we apply a novel model feature structure to represent Chinese semantic relations, which is formalized as ＂recursive directed graph＂. We focus on Chinese special sentence patterns, including the complex noun phrase, verb-complement structure, pivotal sentences, serial verb sentence and subject-predicate predicate sentence. Feature structure facilitates a richer Chinese semantic information extraction when compared with dependency structure. The results show that using recursive directed graph is more suitable for extracting Chinese complex semantic relations.

A survey on facial image deblurring

When a facial image is blurred,it significantly affects high-level vision tasks such as face recognition.The purpose of facial image deblurring is to recover a clear image from a blurry input image,which can improve the recognition accuracy,etc.However,general deblurring methods do not perform well on facial images.Therefore,some face deblurring methods have been proposed to improve performance by adding semantic or structural information as specific priors according to the characteristics of the facial images.In this paper,we survey and summarize recently published methods for facial image deblurring,most of which are based on deep learning.First,we provide a brief introduction to the modeling of image blurring.Next,we summarize face deblurring methods into two categories:model-based methods and deep learning-based methods.Furthermore,we summarize the datasets,loss functions,and performance evaluation metrics commonly used in the neural network training process.We show the performance of classical methods on these datasets and metrics and provide a brief discussion on the differences between model-based and learning-based methods.Finally,we discuss the current challenges and possible future research directions.