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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Patent•
30 Oct 1997TL;DR: An annotation input section reads from a text file the individual data items constituting an annotation pasted in a specified annotation position on a text as a result of the annotation being inputted from an annotation input window and acquires the input positions of the annotations and information on their size.
Abstract: An annotation input section reads from a text file the individual data items constituting an annotation pasted in a specified annotation position on a text as a result of the annotation being inputted from an annotation input window and acquires the input positions of the annotation data items and information on their size. A annotation image magnification change section produces a reduced or enlarged image of the display image of the annotation on the basis of the input positions of the individual annotation data items in the annotation and information on their size acquired by the annotation input section and a specified magnification indicated by a magnification storage section. A text and annotation image display section produces a display image by putting the reduced or enlarged display image of the annotation in the user-specified annotation position on a text display image and displays the display image on the text display screen of a display unit.
46 citations
01 Dec 2013
TL;DR: This paper model the label correlations using the relational graph, and proposes a novel graph structured sparse learning model to incorporate the topological constraints of relation graph in multi-label classifications to improve the annotation results.
Abstract: In multi-label image annotations, because each image is associated to multiple categories, the semantic terms (label classes) are not mutually exclusive. Previous research showed that such label correlations can largely boost the annotation accuracy. However, all existing methods only directly apply the label correlation matrix to enhance the label inference and assignment without further learning the structural information among classes. In this paper, we model the label correlations using the relational graph, and propose a novel graph structured sparse learning model to incorporate the topological constraints of relation graph in multi-label classifications. As a result, our new method will capture and utilize the hidden class structures in relational graph to improve the annotation results. In proposed objective, a large number of structured sparsity-inducing norms are utilized, thus the optimization becomes difficult. To solve this problem, we derive an efficient optimization algorithm with proved convergence. We perform extensive experiments on six multi-label image annotation benchmark data sets. In all empirical results, our new method shows better annotation results than the state-of-the-art approaches.
46 citations
01 Sep 2014
TL;DR: The XML serialization of ISO–LAF, the Graph Annotation Format (GrAF) is described and the rationale behind the various decisions that were made in determining the standard is discussed.
Abstract: This paper overviews the International Standards Organization---Linguistic Annotation Framework (ISO---LAF) developed in ISO TC37 SC4. We describe the XML serialization of ISO---LAF, the Graph Annotation Format (GrAF) and discuss the rationale behind the various decisions that were made in determining the standard. We describe the structure of the GrAF headers in detail and provide multiple examples of GrAF representation for text and multi-media. Finally, we discuss the next steps for standardization of interchange formats for linguistic annotations.
46 citations
Proceedings Article•
01 May 2010TL;DR: The Live Memories corpus is an Italian corpus annotated for anaphoric relations that contains texts from the Italian Wikipedia about the region Trentino/Sud Tirol and from blog sites with users' comments.
Abstract: The Live Memories corpus is an Italian corpus annotated for anaphoric relations. This annotation effort aims to contribute to two significant issues for the CL research: the lack of annotated anaphoric resources for Italian and the increasing interest for the social Web. The Live Memories Corpus contains texts from the Italian Wikipedia about the region Trentino/Sud Tirol and from blog sites with users' comments. It is planned to add a set of articles of local news papers. The corpus includes manual annotated information about morphosyntactic agreement, anaphoricity, and semantic class of the NPs. The anaphoric annotation includes discourse deixis, bridging relations and markes cases of ambiguity with the annotation of alternative interpretations. For the annotation of the anaphoric links the corpus takes into account specific phenomena of the Italian language like incorporated clitics and phonetically non realized pronouns. Reliability studies for the annotation of the mentioned phenomena and for annotation of anaphoric links in general offer satisfactory results. The Wikipedia and blogs dataset will be distributed under Creative Commons Attributions licence.
46 citations
23 Oct 2006
TL;DR: Proceedings of the First Workshop on Semantic Wikis - From Wiki to Semantics co-located with the ESWC2006 Budva, Montenegro, June 12, 2006.
Abstract: Proceedings of the First Workshop on Semantic Wikis - From Wiki to Semantics co-located with the ESWC2006 Budva, Montenegro, June 12, 2006.
46 citations