Boundary Labeling in Text Annotation

Multirow Boundary-Labeling Algorithms for Panorama Images

ACM Transactions on Spatial Algorithms and Systems ◽

10.1145/2794299 ◽

2015 ◽

Vol 1 (1) ◽

pp. 1-30 ◽

Cited By ~ 3

Author(s):

Andreas Gemsa ◽

Jan-Henrik Haunert ◽

Martin Nöllenburg

Keyword(s):

Labeling Algorithms ◽

Boundary Labeling

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Automatic phrase boundary labeling of speech synthesis database using context-dependent HMMs and n-gram prior distributions

10.21437/interspeech.2015-367 ◽

2015 ◽

Author(s):

Qian Chen ◽

Zhen-Hua Ling ◽

Chen-Yu Yang ◽

Li-Rong Dai

Keyword(s):

Speech Synthesis ◽

Prior Distributions ◽

Phrase Boundary ◽

Context Dependent ◽

N Gram ◽

Boundary Labeling

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On the readability of leaders in boundary labeling

Information Visualization ◽

10.1177/1473871618799500 ◽

2018 ◽

Vol 18 (1) ◽

pp. 110-132

Author(s):

Lukas Barth ◽

Andreas Gemsa ◽

Benjamin Niedermann ◽

Martin Nöllenburg

Keyword(s):

Detailed Analysis ◽

Task Performance ◽

User Study ◽

Point Of View ◽

Practical Point ◽

Subjective Preference ◽

Two Measures ◽

Preference Ratings ◽

Boundary Labeling

External labeling deals with annotating features in images with labels that are placed outside of the image and are connected by curves (so-called leaders) to the corresponding features. While external labeling has been extensively investigated from a perspective of automatization, the research on its readability has been neglected. In this article, we present the first formal user study on the readability of leader types in boundary labeling, a special variant of external labeling that considers rectangular image contours. We consider the four most studied leader types (straight, L-shaped, diagonal, and S-shaped) with respect to their performance, that is, whether and how fast a viewer can assign a feature to its label and vice versa. We give a detailed analysis of the results regarding the readability of the four models and discuss their aesthetic qualities based on the users’ preference judgments and interviews. As a consequence of our experiment, we can generally recommend L-shaped leaders as the best compromise between measured task performance and subjective preference ratings, while straight and diagonal leaders received mixed ratings in the two measures. S-shaped leaders are generally not recommended from a practical point of view.

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Development and evaluation of an automatic text annotation system for supporting digital humanities research

Library Hi Tech ◽

10.1108/lht-10-2017-0219 ◽

2019 ◽

Vol 37 (3) ◽

pp. 436-455 ◽

Cited By ~ 2

Author(s):

Chih-Ming Chen ◽

Yung-Ting Chen ◽

Chen-Yu Liu

Keyword(s):

Technology Acceptance ◽

Digital Humanities ◽

Word Segmentation ◽

Ancient Text ◽

Content Type ◽

Text Annotation ◽

Annotation System ◽

Segmentation Process ◽

Automatic Text ◽

Ancient Texts

Purpose An automatic text annotation system (ATAS) that can collect resources from different databases through Linked Data (LD) for automatically annotating ancient texts was developed in this study to support digital humanities research. It allows the humanists referring to resources from diverse databases when interpreting ancient texts as well as provides a friendly text annotation reader for humanists interpreting ancient text through reading. The paper aims to discuss whether the ATAS is helpful to support digital humanities research or not. Design/methodology/approach Based on the quasi-experimental design, the ATAS developed in this study and MARKUS semi-ATAS were compared whether the significant differences in the reading effectiveness and technology acceptance for supporting humanists interpreting ancient text of the Ming dynasty’s collections existed or not. Additionally, lag sequential analysis was also used to analyze users’ operation behaviors on the ATAS. A semi-structured in-depth interview was also applied to understand users’ opinions and perception of using the ATAS to interpret ancient texts through reading. Findings The experimental results reveal that the ATAS has higher reading effectiveness than MARKUS semi-ATAS, but not reaching the statistically significant difference. The technology acceptance of the ATAS is significantly higher than that of MARKUS semi-ATAS. Particularly, the function comparison of the two systems shows that the ATAS presents more perceived ease of use on the functions of term search, connection to source websites and adding annotation than MARKUS semi-ATAS. Furthermore, the reading interface of ATAS is simple and understandable and is more suitable for reading than MARKUS semi-ATAS. Among all the considered LD sources, Moedict, which is an online Chinese dictionary, was confirmed as the most helpful one. Research limitations/implications This study adopted Jieba Chinese parser to perform the word segmentation process based on a parser lexicon for the Chinese ancient texts of the Ming dynasty’s collections. The accuracy of word segmentation to a lexicon-based Chinese parser is limited due to ignoring the grammar and semantics of ancient texts. Moreover, the original parser lexicon used in Jieba Chinese parser only contains the modern words. This will reduce the accuracy of word segmentation for Chinese ancient texts. The two limitations that affect Jieba Chinese parser to correctly perform the word segmentation process for Chinese ancient texts will significantly affect the effectiveness of using ATAS to support digital humanities research. This study thus proposed a practicable scheme by adding new terms into the parser lexicon based on humanists’ self-judgment to improve the accuracy of word segmentation of Jieba Chinese parser. Practical implications Although some digital humanities platforms have been successfully developed to support digital humanities research for humanists, most of them have still not provided a friendly digital reading environment to support humanists on interpreting texts. For this reason, this study developed an ATAS that can automatically retrieve LD sources from different databases on the Internet to supply rich annotation information on reading texts to help humanists interpret texts. This study brings digital humanities research to a new ground. Originality/value This study proposed a novel ATAS that can automatically annotate useful information on an ancient text to increase the readability of the ancient text based on LD sources from different databases, thus helping humanists obtain a deeper and broader understanding in the ancient text. Currently, there is no this kind of tool developed for humanists to support digital humanities research.

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A Literature Research on Machine Learning Techniques used for Training Annotated Corpus

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1063.0882s819 ◽

2019 ◽

Vol 8 (2S8) ◽

pp. 1331-1337

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Automatic Annotation ◽

Research Papers ◽

Text Data ◽

Literature Research ◽

Annotation Task ◽

Text Annotation ◽

Subsequent Research ◽

Learning Techniques

The development of research in the annotation area is growing. Researchers perform annotation task using various forms of datasets such as text, sound, images, and videos. Various algorithms are used to perform tasks. The purpose of this survey is to find out algorithms that are often used by researchers to perform annotation tasks, especially on text data. The literature surveys thirteen research papers on text annotation from the last 5 years. The results of this review indicate that SVM is the algorithm used for all three annotation methods: manual, automatic and semi-automatic annotation, with a significant accuracy above 80%. The result of this survey will be referred by the authors as the basis for subsequent research that will be conducted, especially in the semi-automatic annotation method.

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