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.

Annotating Causal Language Using Corpus Lexicography of Constructions

Abstract: Detecting and analyzing causal language is essential to extracting semantic relationships. To that end, we present an annotation scheme for English causal language (not metaphysical causality), and discuss two methodologies for annotation. The first uses only a coding manual to train annotators in distinguishing causal from non-causal language. To address low inter-coder agreement, we adopted a second methodology, in which we first created a causal language constructicon based on corpus analysis, then required annotators only to annotate instances based on the constructicon. (This resembles the methodology used for annotating the FrameNet and PropBank corpora.) Our contributions, in addition to the annotation scheme itself, are methodological: we discuss when constructicon-based methodology is appropriate, and address the validity of annotation schemes that require expertlevel metalinguistic awareness.

Cross-Ontology multi-level association rule mining in the Gene Ontology.

Abstract: The Gene Ontology (GO) has become the internationally accepted standard for representing function, process, and location aspects of gene products. The wealth of GO annotation data provides a valuable source of implicit knowledge of relationships among these aspects. We describe a new method for association rule mining to discover implicit co-occurrence relationships across the GO sub-ontologies at multiple levels of abstraction. Prior work on association rule mining in the GO has concentrated on mining knowledge at a single level of abstraction and/or between terms from the same sub-ontology. We have developed a bottom-up generalization procedure called Cross-Ontology Data Mining-Level by Level (COLL) that takes into account the structure and semantics of the GO, generates generalized transactions from annotation data and mines interesting multi-level cross-ontology association rules. We applied our method on publicly available chicken and mouse GO annotation datasets and mined 5368 and 3959 multi-level cross ontology rules from the two datasets respectively. We show that our approach discovers more and higher quality association rules from the GO as evaluated by biologists in comparison to previously published methods. Biologically interesting rules discovered by our method reveal unknown and surprising knowledge about co-occurring GO terms.

A Web-based System for Collaborative Annotation of Large Image and Video Collections

Abstract: Annotated collections of images and videos are a necessary basis for the successful development of multimedia retrieval systems. The underlying models of such systems rely heavily on quality and availability of large training collections. The annotation of large collections, however, is a time-consuming and error prone task as it has to be performed by human annotators. In this paper we present the IBM Efficient Video Annotation (EVA) system, a server-based tool for semantic concept annotation of large video and image collections. It is optimised for collaborative annotation and includes features such as workload sharing and support in conducting inter-annotator analysis. We discuss initial results of an ongoing user-evaluation of this system. The results are based on data collected during the 2005 TRECVID Annotation Forum, where more than 100 annotators have been using the system.

Associating annotations with a content source

Abstract: A computing system capable of associating annotations with millions of content sources is described. An annotation is any content associated with a document space. The document space is any document identified by a document identifier. The document space provides the context for the annotation. An annotation is represented as an object having a plurality of properties. The annotation is associated with a content source using a document identifier property. The document identifier property identifies the content source with which the annotation is associated. A scalable computing system for managing annotations responds to requests for presenting annotations to millions of documents a day. The computing system consists of multiple tiers of servers. A tier I server indicates whether there are annotations associated with a content source. A tier II server provides an index to the body of the annotations. A tier III server provides the body of the annotation.