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沈阳师范大学学报（自然科学版） 2020年第4期38卷 351-355页

作者：祁长兴沈阳师范大学软件学院沈阳110034

分时数据的实时走势以及图形分析在金融生产等领域有着广泛的应用,面向这些实时数据的图形化开发也必然有着广泛的应用价值。首先面向金融等领域的分时数据提出了1个图形控件的设计模型,将对数据的处理划分为数据的采集、数据的管理以... 详细信息

分时数据的实时走势以及图形分析在金融生产等领域有着广泛的应用,面向这些实时数据的图形化开发也必然有着广泛的应用价值。首先面向金融等领域的分时数据提出了1个图形控件的设计模型,将对数据的处理划分为数据的采集、数据的管理以及数据的图形化处理3个部分。在此基础上,利用C#语言的GDI技术,设计了一个在.NET平台下可以拖拽的数据显示和分时走势绘制控件,详细阐述了控件中数据的采集、处理以及图形绘制的过程,对应其相关功能设计的相关类和提供的方法以及开发实现技术原理,并且使用该控件对A股数据绘制了股票市场的分时走势图,给出了实现的算法和实现过程,进行了实例化的测试。最后分析了该控件的应用前景及不足。

关键词：分时数据区域走势图图形绘制

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Dependency parsing with dilated iterated graph CNNs 2

Dependency parsing with dilated iterated graph CNNs

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2nd workshop on Structured Prediction for natural language processing, SPNLP 2017, held in conjunction with the Conference on Empirical methods in natural language processing, EMNLP 2017

作者： Strubell, Emma McCallum, Andrew College of Information and Computer Sciences University of Massachusetts Amherst United States

ISBN: (纸本)9781945626937

Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale. Recent advances in GPU hardware have enabled neural networks to achieve significant gains over the previous best models, these models still fail to leverage GPUs' capability for massive parallelism due to their requirement of sequential processing of the sentence. In response, we propose Dilated Iterated graph Convolutional Neural Networks (DIG-CNNs) for graphbased dependency parsing, a graph convolutional architecture that allows for efficient end-to-end GPU parsing. In experiments on the English Penn TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best neural network parsers. © EMNLP *** right reserved.

关键词： Syntactics

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Important attribute identification in knowledge graph

arXiv

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arXiv 2018年

作者： Sun, Shengjie Yang, Dong Zhang, Hongchun Chen, Yanxu Wei, Chao Meng, Xiaonan Hu, Yi Alibaba Group Hangzhou China

The knowledge graph(KG) composed of entities with their descriptions and attributes, and relationship between entities, is finding more and more application scenarios in various natural language processing tasks. In a typical knowledge graph like Wikidata, entities usually have a large number of attributes, but it is difficult to know which ones are important. The importance of attributes can be a valuable piece of information in various applications spanning from information retrieval to natural language generation. In this paper, we propose a general method of using external user generated text data to evaluate the relative importance of an entity's attributes. To be more specific, we use the word/sub-word embedding techniques to match the external textual data back to entities' attribute name and values and rank the attributes by their matching cohesiveness. To our best knowledge, this is the first work of applying vector based semantic matching to important attribute identification, and our method outperforms the previous traditional methods. We also apply the outcome of the detected important attributes to a language generation task;compared with previous generated text, the new method generates much more customized and informative messages. Copyright © 2018, The Authors. All rights reserved.

关键词： Knowledge graph

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Incremental graph-based neural dependency parsing

Incremental graph-based neural dependency parsing

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2017 Conference on Empirical methods in natural language processing, EMNLP 2017

作者： Zheng, Xiaoqing School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (纸本)9781945626838

Very recently, some studies on neural dependency parsers have shown advantage over the traditional ones on a wide variety of languages. However, for graph-based neural dependency parsing systems, they either count on the long-term memory and attention mechanism to implicitly capture the high-order features or give up the global exhaustive inference algorithms in order to harness the features over a rich history of parsing decisions. The former might miss out the important features for specific headword predictions without the help of the explicit structural information, and the latter may suffer from the error propagation as false early structural constraints are used to create features when making future predictions. We explore the feasibility of explicitly taking high-order features into account while remaining the main advantage of global inference and learning for graph-based parsing. The proposed parser first forms an initial parse tree by head-modifier predictions based on the first-order factorization. High-order features (such as grandparent, sibling, and uncle) then can be defined over the initial tree, and used to refine the parse tree in an iterative fashion. Experimental results showed that our model (called INDP) archived competitive performance to existing benchmark parsers on both English and Chinese datasets. © 2017 Association for Computational Linguistics.

关键词： Forecasting

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Neural machine translation with source-side latent graph parsing

Neural machine translation with source-side latent graph par...

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2017 Conference on Empirical methods in natural language processing, EMNLP 2017

作者： Hashimoto, Kazuma Tsuruoka, Yoshimasa University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan

ISBN: (纸本)9781945626838

This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pre-training it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard English-to-Japanese translation dataset. © 2017 Association for Computational Linguistics.

关键词： Neural machine translation

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Scalable processing of Massive Uncertain graph Data: A Simultaneous processing Approach 33

Scalable Processing of Massive Uncertain Graph Data: A Simul...

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IEEE 33rd International Conference on Data Engineering (ICDE)

作者： Zou, Zhaonian Li, Faming Li, Jianzhong Li, Yingshu Harbin Inst Technol Harbin 150001 Heilongjiang Peoples R China Georgia State Univ Atlanta GA 30303 USA

ISBN: (纸本)9781509065431

This paper studies a novel approach to processing massive uncertain graph data. In this approach, we propose a new framework to simultaneously process a query on a set of randomly sampled possible worlds of an uncertain graph. based on this framework, we develop a series of algorithms to analyze massive uncertain graphs, including breadth-first search, shortest distance queries, triangle counting, and core decomposition. We implement this approach based on graphLab, one of the state-of-the-art graph processing frameworks. By sharing fine-grained internal processing steps on common substructures of sampled possible worlds, the new approach achieves tens to hundreds of times speedup in execution time on a cluster of 20 servers.

关键词： Query processing Reactive power Data structures Sampling methods Uncertainty Servers Probabilistic logic

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AMUSE: Multilingual semantic parsing for question answering over linked data

arXiv

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arXiv 2018年

作者： Hakimov, Sherzod Jebbara, Soufian Cimiano, Philipp Bielefeld University Bielefeld33615 Germany

The task of answering natural language questions over RDF data has received wide interest in recent years, in particular in the context of the series of QALD benchmarks. The task consists of mapping a natural language question to an executable form, e.g. SPARQL, so that answers from a given KB can be extracted. So far, most systems proposed are i) monolingual and ii) rely on a set of hard-coded rules to interpret questions and map them into a SPARQL query. We present the first multilingual QALD pipeline that induces a model from training data for mapping a natural language question into logical form as probabilistic inference. In particular, our approach learns to map universal syntactic dependency representations to a language-independent logical form based on DUDES (Dependency-based Underspecified Discourse Representation Structures) that are then mapped to a SPARQL query as a deterministic second step. Our model builds on factor graphs that rely on features extracted from the dependency graph and corresponding semantic *** rely on approximate inference techniques, Markov Chain Monte Carlo methods in particular, as well as Sample Rank to update parameters using a ranking objective. Our focus lies on developing methods that overcome the lexical gap and present a novel combination of machine translation and word embedding approaches for this purpose. As a proof of concept for our approach, we evaluate our approach on the QALD-6 datasets for English, German & Spanish. Copyright © 2018, The Authors. All rights reserved.

关键词： Query processing

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Incremental Generative Models for Syntactic and Semantic natural language processing

Incremental Generative Models for Syntactic and Semantic Nat...

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作者： Jan Moolman Buys University Of Oxford

学位级别：博士

This thesis investigates the role of linguistically-motivated generative models of syntax and semantic structure in natural language processing (NLP). Syn- tactic well-formedness is crucial in language generation, but most statistical models do not account for the hierarchical structure of sentences. Many appli- cations exhibiting natural language understanding rely on structured semantic representations to enable querying, inference and reasoning. Yet most semantic parsers produce domain-specific or inadequately expressive representations. We propose a series of generative transition-based models for dependency syn- tax which can be applied as both parsers and language models while being amenable to supervised or unsupervised learning. Two models are based on Markov assumptions commonly made in NLP: The first is a Bayesian model with hierarchical smoothing, the second is parameterised by feed-forward neu- ral networks. The Bayesian model enables careful analysis of the structure of the conditioning contexts required for generative parsers, but the neural network is more accurate. As a language model the syntactic neural model outperforms both the Bayesian model and n-gram neural networks, pointing to the complementary nature of distributed and structured representations for syntactic prediction. We propose approximate inference methods based on par- ticle filtering. The third model is parameterised by recurrent neural networks (RNNs), dropping the Markov assumptions. Exact inference with dynamic programming is made tractable here by simplifying the structure of the condi- tioning contexts. We then shift the focus to semantics and propose models for parsing sentences to labelled semantic graphs. We introduce a transition-based parser which in- crementally predicts graph nodes (predicates) and edges (arguments). This approach is contrasted against predicting top-down graph traversals. RNNs and pointer networks are key components in approaching graph parsing as an inc

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A User-Centered Segmentation Method for Complex Historical Manuscripts based on Document graphs

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IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2017年第2期47卷 181-193页

作者： Garz, Angelika Seuret, Mathias Fischer, Andreas Ingold, Rolf Univ Fribourg Document Image & Voice Anal Res Grp Dept Informat CH-1700 Fribourg Switzerland Univ Appl Sci Western Switzerland iCoSys Inst CH-1705 Fribourg Switzerland

In historical manuscripts, humans can detect handwritten words, lines, and decorations with lightness even if they do not know the language or the script. Yet for automatic processing this task has proven elusive, especially in the case of handwritten documents with complex layouts, which is why semiautomatic methods that integrate the human user into the process are needed. In this paper, we introduce a user-centered segmentation method based on document graphs and scribbling interaction. The graphs capture a sparse representation of the document's structure that can then be edited by the user with a stylus on a touch-sensitive screen. We evaluate the proposed method on a newly introduced database of historical manuscripts with complex layout and demonstrate, first, that the document graphs are already close to the desired segmentation and, second, that scribbling allows a natural and efficient interaction.

关键词： graph representation ground truth creation historical manuscripts image segmentation pen-based interaction scribbling user-centered systems

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Modeling and extracting research processes

Modeling and extracting research processes

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作者： Pertsas, Vayianos Athens University Economics and Business (AUEB)

学位级别：ET

We define a conceptualization of the research process, the Scholarly Ontology (SO),based on which we show how different aspects of scholarly research behaviour canbe documented automatically as well as interactively and relevant resources, possiblyfrom different fields, can be interconnected. We demonstrate the capacity of SO tosupport various research inquiries by formulating appropriate complex queries. Weexplore the role of type taxonomies, such as the Activity Type class, and explain howtheir instances can function as pivotal elements in modeling intentionality andfunctionality properties of various SO classes. We formulate semantic constraintsthat provide a framework for reasoning over SO. Furthermore, we investigate the useof semantic similarity in process classification via the Activity Type taxonomy. Wereview several graph-based similarity measures and we propose a new, highperformance one based on a novel definition of semantic specificity that leveragesstructural properties of graphs such as WordNet. We then turn to the creation of aknowledge base by information extraction from text, driven by SO. We do this usingtwo approaches: (1) devising specialized NLP rules that fully exploit the ontologyand structural and syntactic properties of scientific publications; and (2) usingstatistical machine learning methods. Along the first line we developed ResearchSpotlight, a system that leverages information from DBpedia, retrieves articles fromvarious sources (e-repositories, Web pages), extracts and interrelates various kindsof named and non-named entities by exploiting article metadata, the structure of textas well as syntactic, lexical and semantic constraints, and populates a knowledgebase in the form of RDF triples. Specialized rules were defined, implemented andembedded in the outputs provided by SPACY, an industrial-strength framework forNLP tasks such as tokenization, segmentation, POS tagging and dependency *** of the system is performed thro

关键词： Οντολογίες Μηχανική μάθηση Εξαγωγή Πληροφορίας από Κείμενο Επεξεργασία φυσικής γλώσσας Εννοιολογική μοντελοποίηση Domain ontology Machine learning natural language processing Information extraction text minning Conceptual modeling process model

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