Towards training naïve participants for a perceptual annotation task designed for experts

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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A WIKIPEDIA-BASED FRAMEWORK FOR COLLABORATIVE SEMANTIC ANNOTATION

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213011000413 ◽

2011 ◽

Vol 20 (05) ◽

pp. 847-886 ◽

Cited By ~ 1

Author(s):

N. FERNÁNDEZ ◽

J. A. FISTEUS ◽

D. FUENTES ◽

L. SÁNCHEZ ◽

V. LUQUE

Keyword(s):

Semantic Web ◽

Data Processing ◽

Web Search ◽

Semantic Annotation ◽

Formal Ontology ◽

Manual Annotation ◽

Annotation Task ◽

Web Resources ◽

User Actions ◽

Common User

The semantic web aims at automating web data processing tasks that nowadays only humans are able to do. To make this vision a reality, the information on web resources should be described in a computer-meaningful way, in a process known as semantic annotation. In this paper, a manual, collaborative semantic annotation framework is described. It is designed to take advantage of the benefits of manual annotation systems (like the possibility of annotating formats difficult to annotate in an automatic manner) addressing at the same time some of their limitations (reduce the burden for non-expert annotators). The framework is inspired by two principles: use Wikipedia as a facade for a formal ontology and integrate the semantic annotation task with common user actions like web search. The tools in the framework have been implemented, and empirical results obtained in experiences carried out with these tools are reported.

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Challenges of an Annotation Task for Open Information Extraction in Portuguese

Lecture Notes in Computer Science - Computational Processing of the Portuguese Language ◽

10.1007/978-3-319-99722-3_7 ◽

2018 ◽

pp. 66-76 ◽

Cited By ~ 1

Author(s):

Rafael Glauber ◽

Leandro Souza de Oliveira ◽

Cleiton Fernando Lima Sena ◽

Daniela Barreiro Claro ◽

Marlo Souza

Keyword(s):

Information Extraction ◽

Annotation Task ◽

Open Information Extraction

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Leveraging Psycholinguistic Resources and Emotional Sequence Models for Suicide Note Emotion Annotation

Biomedical Informatics Insights ◽

10.4137/bii.s8979 ◽

2012 ◽

Vol 5s1 ◽

pp. BII.S8979 ◽

Cited By ~ 3

Author(s):

Eric Yeh ◽

William Jarrold ◽

Joshua Jordan

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Annotation Task ◽

Sentence Level ◽

Machine Learning Approach ◽

Suicide Note

We describe the submission entered by SRI International and UC Davis for the I2B2 NLP Challenge Track 2. Our system is based on a machine learning approach and employs a combination of lexical, syntactic, and psycholinguistic features. In addition, we model the sequence and locations of occurrence of emotions found in the notes. We discuss the effect of these features on the emotion annotation task, as well as the nature of the notes themselves. We also explore the use of bootstrapping to help account for what appeared to be annotator fatigue in the data. We conclude a discussion of future avenues for improving the approach for this task, and also discuss how annotations at the word span level may be more appropriate for this task than annotations at the sentence level.

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