User-oriented visual layout at multiple granularities

1996 ◽  
Author(s):  
Yannis Ioannidis ◽  
Miron Livny ◽  
Jian Bao ◽  
Eben M. Haber
Keyword(s):  
Multiple Granularities

scholarly journals Incremental Construction of Generalized Voronoi Diagrams on Pointerless Quadtrees

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Quanjun Yin ◽  
Long Qin ◽  
Xiaocheng Liu ◽  
Yabing Zha
Keyword(s):  
Voronoi Diagrams ◽  
Surrounding Area ◽  
Repair Mechanism ◽  
Local Repair ◽  
Order Of Magnitude ◽  
Generalized Voronoi Diagrams ◽  
Incremental Construction ◽  
Coarse To Fine ◽  
Multiple Granularities ◽  
Magnitude Improvement

In robotics, Generalized Voronoi Diagrams (GVDs) are widely used by mobile robots to represent the spatial topologies of their surrounding area. In this paper we consider the problem of constructing GVDs on discrete environments. Several algorithms that solve this problem exist in the literature, notably the Brushfire algorithm and its improved versions which possess local repair mechanism. However, when the area to be processed is very large or is of high resolution, the size of the metric matrices used by these algorithms to compute GVDs can be prohibitive. To address this issue, we propose an improvement on the current algorithms, using pointerless quadtrees in place of metric matrices to compute and maintain GVDs. Beyond the construction and reconstruction of a GVD, our algorithm further provides a method to approximate roadmaps in multiple granularities from the quadtree based GVD. Simulation tests in representative scenarios demonstrate that, compared with the current algorithms, our algorithm generally makes an order of magnitude improvement regarding memory cost when the area is larger than210×210. We also demonstrate the usefulness of the approximated roadmaps for coarse-to-fine pathfinding tasks.

scholarly journals Desiderata for Controlled Medical Vocabularies in the Twenty-First Century

1998 ◽  
Vol 37 (04/05) ◽  
pp. 394-403 ◽  
Author(s):  
J. J. Cimino
Keyword(s):  
Medical Informatics ◽  
First Century ◽  
Medical Informatics Applications ◽  
Formal Definitions ◽  
The Common ◽  
Twenty First Century ◽  
Multiple Granularities ◽  
Do So

AbstractBuilders of medical informatics applications need controlled medical vocabularies to support their applications and it is to their advantage to use available standards. In order to do so, however, these standards need to address the requirements of their intended users. Overthe past decade, medical informatics researchers have begun to articulate some of these requirements. This paper brings together some of the common themes which have been described, including: vocabulary content, concept orientation, concept permanence, nonsemantic concept identifiers, polyhierarchy, formal definitions, rejection of “not elsewhere classified” terms, multiple granularities, mUltiple consistent views, context representation, graceful evolution, and recognized redundancy. Standards developers are beginning to recognize and address these desiderata and adapt their offerings to meet them.

Collective Entity Disambiguation Based on Hierarchical Semantic Similarity

2020 ◽  
Vol 16 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Bingjing Jia ◽  
Hu Yang ◽  
Bin Wu ◽  
Ying Xing
Keyword(s):  
Semantic Similarity ◽  
Semantic Matching ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Global Features ◽  
Global Coherence ◽  
Similarity Model ◽  
Benchmark Datasets ◽  
Entity Disambiguation ◽  
Multiple Granularities

Entity disambiguation involves mapping mentions in texts to the corresponding entities in a given knowledge base. Most previous approaches were based on handcrafted features and failed to capture semantic information over multiple granularities. For accurately disambiguating entities, various information aspects of mentions and entities should be used in. This article proposes a hierarchical semantic similarity model to find important clues related to mentions and entities based on multiple sources of information, such as contexts of the mentions, entity descriptions and categories. This model can effectively measure the semantic matching between mentions and target entities. Global features are also added, including prior popularity and global coherence, to improve the performance. In order to verify the effect of hierarchical semantic similarity model combined with global features, named HSSMGF, experiments were carried out on five publicly available benchmark datasets. Results demonstrate the proposed method is very effective in the case that documents have more mentions.

Discovering frequent event patterns with multiple granularities in time sequences

1998 ◽  
Vol 10 (2) ◽  
pp. 222-237 ◽  
Author(s):  
C. Bettini ◽  
X.S. Wang ◽  
S. Jajodia ◽  
J.-L. Lin
Keyword(s):  
Frequent Event ◽  
Multiple Granularities ◽  
Time Sequences

scholarly journals Mostly-unsupervised statistical segmentation of Japanese kanji sequences

2003 ◽  
Vol 9 (2) ◽  
pp. 127-149 ◽  
Author(s):  
RIE KUBOTA ANDO ◽  
LILLIAN LEE
Keyword(s):  
State Of The Art ◽  
Training Data ◽  
Syntactic Analysis ◽  
Annotation Scheme ◽  
Error Metrics ◽  
Japanese Word ◽  
Robust Statistical Method ◽  
Segmentation Algorithms ◽  
Statistical Segmentation ◽  
Multiple Granularities

Given the lack of word delimiters in written Japanese, word segmentation is generally considered a crucial first step in processing Japanese texts. Typical Japanese segmentation algorithms rely either on a lexicon and syntactic analysis or on pre-segmented data; but these are labor-intensive, and the lexico-syntactic techniques are vulnerable to the unknown word problem. In contrast, we introduce a novel, more robust statistical method utilizing unsegmented training data. Despite its simplicity, the algorithm yields performance on long kanji sequences comparable to and sometimes surpassing that of state-of-the-art morphological analyzers over a variety of error metrics. The algorithm also outperforms another mostly-unsupervised statistical algorithm previously proposed for Chinese. Additionally, we present a two-level annotation scheme for Japanese to incorporate multiple segmentation granularities, and introduce two novel evaluation metrics, both based on the notion of a compatible bracket, that can account for multiple granularities simultaneously.

scholarly journals Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention

2018 ◽  
Author(s):  
Ling Luo ◽  
Xiang Ao ◽  
Feiyang Pan ◽  
Jin Wang ◽  
Tong Zhao ◽  
...  
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Neural Net ◽  
Financial Decisions ◽  
Financial Applications ◽  
Decision Making Processes ◽  
Potential Risks ◽  
Net Framework ◽  
Multiple Granularities

Sentiment analysis has played a significant role in financial applications in recent years. The informational and emotive aspects of news texts may affect the prices, volatilities, volume of trades, and even potential risks of financial subjects. Previous studies in this field mainly focused on identifying polarity~(e.g. positive or negative). However, as financial decisions broadly require justifications, only plausible polarity cannot provide enough evidence during the decision making processes of humanity. Hence an explainable solution is in urgent demand. In this paper, we present an interpretable neural net framework for financial sentiment analysis. First, we design a hierarchical model to learn the representation of a document from multiple granularities. In addition, we propose a query-driven attention mechanism to satisfy the unique characteristics of financial documents. With the domain specified questions provided by the financial analysts, we can discover different spotlights for queries from different aspects. We conduct extensive experiments on a real-world dataset. The results demonstrate that our framework can learn better representation of the document and unearth meaningful clues on replying different users? preferences. It also outperforms the state-of-the-art methods on sentiment prediction of financial documents. 

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