Topic
Annotation
About: Annotation is a research topic. Over the lifetime, 6719 publications have been published within this topic receiving 203463 citations. The topic is also known as: note & markup.
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04 Jun 2009
TL;DR: The GENIA corpus as mentioned in this paper is an extension of the GENIA annotation which integrates GENETAG-style gene annotation, which is often associated with a function, while GENIA concentrates on the physical forms of gene.
Abstract: Proteins and genes are the most important entities in molecular biology, and their automated recognition in text is the most widely studied task in biomedical information extraction (IE). Several corpora containing annotation for these entities have been introduced, GENIA (Kim et al., 2003; Kim et al., 2008) and GENETAG (Tanabe et al., 2005) being the most prominent and widely applied. While both aim to address protein/gene annotation, their annotation principles differ notably. One key difference is that GENETAG annotates the conceptual entity, gene, which is often associated with a function, while GENIA concentrates on the physical forms of gene, i.e. protein, DNA and RNA. The difference has caused serious problems relating to the compatibility and comparability of the annotations. In this work, we present an extension of GENIA annotation which integrates GENETAG-style gene annotation. The new version of the GENIA corpus is the first to bring together these two types of entity annotation.
42 citations
TL;DR: The utility of the toolkit is illustrated using annotation of a pairwise comparison of the mouse MHC class II and class III regions with orthologous human sequences to identify conserved, noncoding sequences that are DNase I hypersensitive sites in chromatin of mouse cells.
42 citations
18 Mar 2001
TL;DR: The technique of Virtual Annotation is presented as a specialization of Predictive Annotation for answering definitional What is questions, which generally have the property that the type of the answer is not given away by the question.
Abstract: We present the technique of Virtual Annotation as a specialization of Predictive Annotation for answering definitional What is questions These questions generally have the property that the type of the answer is not given away by the question, which poses problems for a system which has to select answer strings from suggested passages Virtual Annotation uses a combination of knowledge-based techniques using an ontology, and statistical techniques using a large corpus to achieve high precision
42 citations
17 Oct 2016
TL;DR: This paper shows that atlas forests extended by a novel superpixel-based confidence measure are well-suited for medical instrument segmentation in laparoscopic video data and demonstrates that the new algorithm and the crowd can mutually benefit from each other in a collaborative annotation process.
Abstract: With the recent breakthrough success of machine learning based solutions for automatic image annotation, the availability of reference image annotations for algorithm training is one of the major bottlenecks in medical image segmentation and many other fields. Crowdsourcing has evolved as a valuable option for annotating large amounts of data while sparing the resources of experts, yet, segmentation of objects from scratch is relatively time-consuming and typically requires an initialization of the contour. The purpose of this paper is to investigate whether the concept of crowd-algorithm collaboration can be used to simultaneously (1) speed up crowd annotation and (2) improve algorithm performance based on the feedback of the crowd. Our contribution in this context is two-fold: Using benchmarking data from the MICCAI 2015 endoscopic vision challenge we show that atlas forests extended by a novel superpixel-based confidence measure are well-suited for medical instrument segmentation in laparoscopic video data. We further demonstrate that the new algorithm and the crowd can mutually benefit from each other in a collaborative annotation process. Our method can be adapted to various applications and thus holds high potential to be used for large-scale low-cost data annotation.
42 citations
Patent•
10 Dec 1996
TL;DR: In this article, a method of annotation in an electronic book (118) includes reading machine-readable data from a machinereadable storage medium (136) installed in the electronic book and displaying a page of text represented by the machinereadable data.
Abstract: A method of annotation in an electronic book (118) includes reading machine-readable data from a machine-readable storage medium (136) installed in the electronic book (118), and displaying a page of text represented by the machine-readable data. The electronic book (118) receives a user-initiated event (342) in which a portion of the text (330) is selected; and an annotation (344) associated with the portion of the text (330). An indicator of the portion (330) of the text and data representative of the annotation is stored in the electronic book (118). A second page of the text other than the page of the text containing the portion (330) is displayed, and thereafter, the page of the text containing the portion (330) indicated by the indicator is redisplayed. A note marker icon (370) is displayed to indicate that the page has the annotation associated therewith.
42 citations