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Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension 2

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Extremely Short Chinese Text Classification Method Based on ...

2019 2nd International Symposium on Big data and Applied Statistics, ISBDAS 2019

作者： Yue, Yongzeng Zhang, Yuhong Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data [Hefei University of Technology] Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei23061 China

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness;secondly, the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

Quantifying the conceptual error in dimensionality reduction

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arXiv 2021年

作者： Hanika, Tom Hirth, Johannes Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany

Dimension reduction of data sets is a standard problem in the realm of machine learning and knowledge reasoning. They affect patterns in and dependencies on data dimensions and ultimately influence any decision-making processes. Therefore, a wide variety of reduction procedures are in use, each pursuing different objectives. A so far not considered criterion is the conceptual continuity of the reduction mapping, i.e., the preservation of the conceptual structure with respect to the original data set. Based on the notion scale-measure from formal concept analysis we present in this work a) the theoretical foundations to detect and quantify conceptual errors in data scalings;b) an experimental investigation of our approach on eleven data sets that were respectively treated with a variant of non-negative matrix *** Codes 03G10, 68T27, 68T30, 06B10, 06A15, 06-08 Copyright © 2021, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

Attribute selection using contranominal scales

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arXiv 2021年

作者： Dürrschnabel, Dominik Koyda, Maren Stumme, Gerd Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany

Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data;however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties. The size of such a lattice depends on the number of subcontexts in the corresponding formal context that are isomorphic to a contranominal scale of high dimension. In this work, we propose the algorithm ContraFinder that enables the computation of all contranominal scales of a given formal context. Leveraging this algorithm, we introduce δ-adjusting, a novel approach in order to decrease the number of contranominal scales in a formal context by the selection of an appropriate attribute subset. We demonstrate that δ-adjusting a context reduces the size of the hereby emerging sub-semilattice and that the implication set is restricted to meaningful implications. This is evaluated with respect to its associated knowledge by means of a classification task. Hence, our proposed technique strongly improves understandability while preserving important conceptual *** Codes 68T30, 03G10. Copyright © 2021, The Authors. All rights reserved.

关键词： Information analysis

Exploring scale-measures of data sets

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arXiv 2021年

作者： Hanika, Tom Hirth, Johannes Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany

Measurement is a fundamental building block of numerous scientific models and their creation. This is in particular true for data driven science. Due to the high complexity and size of modern data sets, the necessity for the development of understandable and efficient scaling methods is at hand. A profound theory for scaling data is scale-measures, as developed in the field of formal concept analysis. Recent developments indicate that the set of all scale-measures for a given data set constitutes a lattice and does hence allow efficient exploring algorithms. In this work we study the properties of said lattice and propose a novel scale-measure exploration algorithm that is based on the well-known and proven attribute exploration approach. Our results motivate multiple applications in scale recommendation, most prominently (semi-)automatic *** Codes 03G10, 68T27, 68T30, 06B10, 06A15, 06-08 Copyright © 2021, The Authors. All rights reserved.

关键词： Formal concept analysis

Graph-based exercise- And knowledge-aware learning network for student performance prediction

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arXiv 2021年

作者： Liu, Mengfan Shao, Pengyang Zhang, Kun Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China

Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students' knowledge level and provide personalized teaching strategies for them. Researchers have made plenty of efforts on this task. They either leverage educational psychology methods to predict students' scores according to the learned knowledge proficiency, or make full use of Collaborative Filtering (CF) models to represent latent factors of students and exercises. However, most of these methods either neglect the exercise-specific characteristics (e.g., exercise materials), or cannot fully explore the high-order interactions between students, exercises, as well as knowledge concepts. To this end, we propose a Graph-based Exercise- and knowledge-Aware Learning Network for accurate student score prediction. Specifically, we learn students' mastery of exercises and knowledge concepts respectively to model the two-fold effects of exercises and knowledge concepts. Then, to model the high-order interactions, we apply graph convolution techniques in the prediction process. Extensive experiments on two real-world datasets prove the effectiveness of our proposed Graph-EKLN. Copyright © 2021, The Authors. All rights reserved.

关键词： Students

A Coffee-Ring Effect-Based Paper Sensor Chip for the Determination of Β-Lactoglobulin in Foods Via a Smartphone

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SSRN

SSRN 2022年

作者： Lu, Yang Yang, Jingyi Zhang, Yong Wu, Lin State Key Laboratory for Food Nutrition and Safety College of Food Science and Engineering Tianjin University of Science and Technology Tianjin300457 China State Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

To determine allergen β-lactoglobulin (β-LG) in foods, a method by a coffee-ring effect (CRE)-based paper sensor chip combined with a smartphone has been developed. The strategy was based on the principle of fluorescence quenching by the quantum dot-aptamer-graphene oxide (QD-Apt-GO) and where the degree of quenching was inversely proportional to the concentration of β-LG. First, the prepared β-LG solution was added to the QD-aptamer fluorescent probe solution followed by an appropriate amount of GO. After the reaction, a certain amount of the mixture was dispensed onto the detection zone of the sensor chip, resulting in the spread of the analyte solution by evaporation-driven flow. The QD-aptamer and the QD-Apt-β-LG in the mixed solution formed a visible fluorescent coffee-ring. For signal quantitation, an image of the coffee-ring was taken by a smartphone and imported into self-programmed software for analysis. The quantitative results for β-LG were acquired rapidly based on the magnitude of the generated fluorescent coffee-ring. The limit of detection (LOD) for β-LG was 0.048 mg L -1 and the linear range for the optimized assay was 0.39–1000 mg L -1 . The method was validated for analysis of real food samples and the results were consistent with those of a commercial ELISA assay kit. The smartphone-based assay provides a simple, economic, compact, and visual detection platform for the determination of β-LG in foods. © 2022, The Authors. All rights reserved.

关键词： Graphene

Boolean substructures in formal concept analysis

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arXiv 2021年

作者： Koyda, Maren Stumme, Gerd Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany

It is known that a (concept) lattice contains an n-dimensional Boolean suborder if and only if the context contains an n-dimensional contra-nominal scale as subcontext. In this work, we investigate more closely the interplay between the Boolean subcontexts of a given finite context and the Boolean suborders of its concept lattice. To this end, we define mappings from the set of subcontexts of a context to the set of suborders of its concept lattice and vice versa and study their structural properties. In addition, we introduce closed-subcontexts as an extension of closed relations to investigate the set of all sublattices of a given *** Codes 03G10 (Primary) 06B05 (Secondary). Copyright © 2021, The Authors. All rights reserved.

关键词： Information analysis

Triadic exploration and exploration with multiple experts

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arXiv 2021年

作者： Felde, Maximilian Stumme, Gerd Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany

Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain. Copyright © 2021, The Authors. All rights reserved.

关键词： Formal concept analysis

Force-directed layout of order diagrams using dimensional reduction

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arXiv 2021年

作者： Dürrschnabel, Dominik Stumme, Gerd Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany

Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an experienced expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one optimizes on distances of nodes and the other on distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of *** Codes 68R10, 06A07. Copyright © 2021, The Authors. All rights reserved.

关键词： Drawing (graphics)