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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.


Papers
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01 Jan 2003
TL;DR: The SPAAC (SPeech Act Annotated Corpus) scheme has been developed to annotate a range of different kinds of dialogue, but within the general scope of telephone task-oriented dialogue between two people, as contrasted, for example, with general conversation.
Abstract: 1. Introduction The annotation of dialogues in terms of speech acts (or dialogue acts) has typically followed one of two paths. One path has been to tailor the speech act categories to a specific task or domain, as in the Edinburgh Map Task annotation scheme (Carletta et al. 1995). Another has been to aim at a more general coverage of dialogue, as in the DAMSL (Dialogue Act Markup in Several Layers) annotation scheme (Allen and Core 1997). We report here on a pilot project (supported by EPSRC grant GR/R37142/01) which has attempted to achieve a middle ground between the aims of genericity and specificity in data coverage. The SPAAC (SPeech Act Annotated Corpus) scheme has been developed to annotate a range of different kinds of dialogue, but within the general scope of telephone task-oriented dialogue between two people, as contrasted, for example, with general conversation. To try out the generic potential of the scheme, we have annotated different kinds of dialogue, especially British Telecom and The Trainline service-providing dialogues. 1 A set of 41 speech-act categories has been applied to these sets of corpus data.

48 citations

01 Aug 2013
TL;DR: The Arborator is presented, an online tool for collaborative dependency annotation together with a case study of crowdsourcing in a pedagogical university context and existing tools for dependency annotation as well as the distinctive features and design choices of the tool.
Abstract: This paper presents the Arborator, an online tool for collaborative dependency annotation together with a case study of crowdsourcing in a pedagogical university context. In greater detail, we explore what generally distinguishes dependency annotation tools from phrase structure annotation tools and we introduce existing tools for dependency annotation as well as the distinctive features and design choices of our tool. Finally we show how to setup a crowdsourced dependency annotation experiment as an exercise for university students. We explore constraints, results, and conclu-

47 citations

Book ChapterDOI
25 Jun 2008
TL;DR: A system for ontology based annotation and indexing of biomedical data to provide a service that enables users to locate biomedical data resources related to particular ontology concepts and to provide the user with integrated results from different biomedical resource in one place.
Abstract: We present a system for ontology based annotation and indexing of biomedical data; the key functionality of this system is to provide a service that enables users to locate biomedical data resources related to particular ontology concepts. The system's indexing workflow processes the text metadata of diverse resource elements such as gene expression data sets, descriptions of radiology images, clinical-trial reports, and PubMed article abstracts to annotate and index them with concepts from appropriate ontologies. The system enables researchers to search biomedical data sources using ontology concepts. What distinguishes this work from other biomedical search tools is:(i) the use of ontology semantics to expand the initial set of annotations automatically generated by a concept recognition tool; (ii) the unique ability to use almost all publicly available biomedical ontologies in the indexing workflow; (iii) the ability to provide the user with integrated results from different biomedical resource in one place. We discuss the system architecture as well as our experiences during its prototype implementation (http://www.bioontology.org/tools.html).

47 citations

Proceedings ArticleDOI
18 Apr 2006
TL;DR: A generic semantic annotation model for 3D, called 3DSEAM, is used, which aims at indexing 3D contents considering visual, geometric and semantic aspects and a generic 3D Annotation Framework is proposed in order to manage the semantic annotations of 3D objects.
Abstract: The continuous evolution of computer capacities, as well as the emergence of the X3D standard has recently boosted the 3D domain. Even if efficient tools that support the designer's work exist, little attention is paid to the reuse of 3D models. Associating some semantics with 3D contents is an important issue for reusing such contents or pieces of content. In this paper, we address this issue by using a generic semantic annotation model for 3D, called 3DSEAM [Bilasco et al. 2005b] (3D SEmantics Annotation Model). 3DSEAM aims at indexing 3D contents considering visual, geometric and semantic aspects. A generic 3D Annotation Framework (called 3DAF) is proposed in order to manage the semantic annotations of 3D objects. 3DAF is instantiated using an MPEG-7-based architecture. An extension of MPEG-7 that addresses 3D content is used. [Bilasco et al. 2005a].

47 citations

Proceedings Article
01 Feb 2009
TL;DR: A new method is proposed which extracts a sample of subwindows from a set of annotated images in order to train a subwindow annotation model by using the extremely randomized trees ensemble method appropriately extended to handle high-dimensional output spaces.
Abstract: This paper addresses image annotation, i.e. labelling pixels of an image with a class among a finite set of predefined classes. We propose a new method which extracts a sample of subwindows from a set of annotated images in order to train a subwindow annotation model by using the extremely randomized trees ensemble method appropriately extended to handle high-dimensional output spaces. The annotation of a pixel of an unseen image is done by aggregating the annotations of its subwindows containing this pixel. The proposed method is compared to a more basic approach predicting the class of a pixel from a single window centered on that pixel and to other state-of-the-art image annotation methods. In terms of accuracy, the proposed method significantly outperforms the basic method and shows good performances with respect to the state-of-the-art, while being more generic, conceptually simpler, and of higher computational efficiency than these latter.

47 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,461
20223,073
2021305
2020401
2019383
2018373