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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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Journal ArticleDOI
TL;DR: The FANTOM3 annotation system, consisting of automated computational prediction, manualCuration, and final expert curation, facilitated the comprehensive characterization of the mouse transcriptome, and could be applied to the transcriptomes of other species.
Abstract: The international FANTOM consortium aims to produce a comprehensive picture of the mammalian transcriptome, based upon an extensive cDNA collection and functional annotation of full-length enriched cDNAs. The previous dataset, FANTOM2, comprised 60,770 full-length enriched cDNAs. Functional annotation revealed that this cDNA dataset contained only about half of the estimated number of mouse protein-coding genes, indicating that a number of cDNAs still remained to be collected and identified. To pursue the complete gene catalog that covers all predicted mouse genes, cloning and sequencing of full-length enriched cDNAs has been continued since FANTOM2. In FANTOM3, 42,031 newly isolated cDNAs were subjected to functional annotation, and the annotation of 4,347 FANTOM2 cDNAs was updated. To accomplish accurate functional annotation, we improved our automated annotation pipeline by introducing new coding sequence prediction programs and developed a Web-based annotation interface for simplifying the annotation procedures to reduce manual annotation errors. Automated coding sequence and function prediction was followed with manual curation and review by expert curators. A total of 102,801 full-length enriched mouse cDNAs were annotated. Out of 102,801 transcripts, 56,722 were functionally annotated as protein coding (including partial or truncated transcripts), providing to our knowledge the greatest current coverage of the mouse proteome by full-length cDNAs. The total number of distinct non-protein-coding transcripts increased to 34,030. The FANTOM3 annotation system, consisting of automated computational prediction, manual curation, and final expert curation, facilitated the comprehensive characterization of the mouse transcriptome, and could be applied to the transcriptomes of other species.

193 citations

Proceedings ArticleDOI
Philip V. Ogren1
04 Jun 2006
TL;DR: A general-purpose text annotation tool called Knowtator is introduced that facilitates the manual creation of annotated corpora that can be used for evaluating or training a variety of natural language processing systems.
Abstract: A general-purpose text annotation tool called Knowtator is introduced. Knowtator facilitates the manual creation of annotated corpora that can be used for evaluating or training a variety of natural language processing systems. Building on the strengths of the widely used Protege knowledge representation system, Knowtator has been developed as a Protege plug-in that leverages Protege's knowledge representation capabilities to specify annotation schemas. Knowtator's unique advantage over other annotation tools is the ease with which complex annotation schemas (e.g. schemas which have constrained relationships between annotation types) can be defined and incorporated into use. Knowtator is available under the Mozilla Public License 1.1 at http://bionlp.sourceforge.net/Knowtator.

193 citations

Proceedings ArticleDOI
10 May 2005
TL;DR: C-PANKOW (Context-driven PankOW), which alleviates several shortcomings of PANKOW and uses the annotation context in order to distinguish the significance of a pattern match for the given annotation task.
Abstract: Without the proliferation of formal semantic annotations, the Semantic Web is certainly doomed to failure. In earlier work we presented a new paradigm to avoid this: the 'Self Annotating Web', in which globally available knowledge is used to annotate resources such as web pages. In particular, we presented a concrete method instantiating this paradigm, called PANKOW (Pattern-based ANnotation through Knowledge On the Web). In PANKOW, a named entity to be annotated is put into several linguistic patterns that convey competing semantic meanings. The patterns that are matched most often on the Web indicate the meaning of the named entity --- leading to automatic or semi-automatic annotation.In this paper we present C-PANKOW (Context-driven PANKOW), which alleviates several shortcomings of PANKOW. First, by downloading abstracts and processing them off-line, we avoid the generation of large number of linguistic patterns and correspondingly large number of Google queries.Second, by linguistically analyzing and normalizing the downloaded abstracts, we increase the coverage of our pattern matching mechanism and overcome several limitations of the earlier pattern generation process. Third, we use the annotation context in order to distinguish the significance of a pattern match for the given annotation task. Our experiments show that C-PANKOW inherits all the advantages of PANKOW (no training required etc.), but in addition it is far more efficient and effective.

193 citations

Proceedings ArticleDOI
19 Jun 2008
TL;DR: A corpus annotation project that has produced a freely available resource for research on handling negation and uncertainty in biomedical texts and is called the BioScope corpus, which consists of medical free texts, biological full papers and biological scientific abstracts.
Abstract: This article reports on a corpus annotation project that has produced a freely available resource for research on handling negation and uncertainty in biomedical texts (we call this corpus the BioScope corpus). The corpus consists of three parts, namely medical free texts, biological full papers and biological scientific abstracts. The dataset contains annotations at the token level for negative and speculative keywords and at the sentence level for their linguistic scope. The annotation process was carried out by two independent linguist annotators and a chief annotator -- also responsible for setting up the annotation guidelines -- who resolved cases where the annotators disagreed. We will report our statistics on corpus size, ambiguity levels and the consistency of annotations.

191 citations

Proceedings Article
23 Jun 2018
TL;DR: INCEpTION is a new annotation platform for tasks including interactive and semantic annotation (e.g., concept linking, fact linking, knowledge base population, semantic frame annotation) that incorporates machine learning capabilities which actively assist and guide annotators.
Abstract: We introduce INCEpTION, a new annotation platform for tasks including interactive and semantic annotation (eg, concept linking, fact linking, knowledge base population, semantic frame annotation) These tasks are very time consuming and demanding for annotators, especially when knowledge bases are used We address these issues by developing an annotation platform that incorporates machine learning capabilities which actively assist and guide annotators The platform is both generic and modular It targets a range of research domains in need of semantic annotation, such as digital humanities, bioinformatics, or linguistics INCEpTION is publicly available as open-source software

190 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