We present the Omega ontology, a large terminological ontology obtained by remerging WordNet and Mikrokosmos, adding information from various other sources, and subordinating the result to a newly designed feature-oriented upper model. We explain the organizing principles of the representation used for Omega and discuss the methodology used to merge the constituent conceptual hierarchies. We survey a range of auxiliary knowledge sources (including instances, verb frame annotations, and domainspecific sub-ontologies) incorporated into the basic conceptual structure and applications that have benefited from Omega. Omega is available for browsing at

Abstract not found

Annotation projects dealing with complex semantic or pragmatic phenomena face the dilemma of creating annotation schemes that oversimplify the phenomena, or that capture distinctions conventional reliability metrics cannot measure adequately. The solution to the dilemma is to develop metrics that quantify the decisions that annotators are asked to make. This paper discusses MASI, distance metric for comparing sets, and illustrates its use in quantifying the reliability of a specific dataset. Annotations of Summary Content Units (SCUs) generate models referred to as pyramids which can be used to evaluate unseen human summaries or machine summaries. The paper presents reliability results for five pairs of pyramids created for document sets from the 2003 Document Understanding Conference (DUC). The annotators worked independently of each other. Differences between application of MASI to pyramid annotation and its previous application to co-reference annotation are discussed. In addition, it is argued that a paradigmatic reliability study should relate measures of inter-annotator agreement to independent assessments, such as significance tests of the annotated variables with respect to other phenomena. In effect, what counts as sufficiently reliable intera-annotator agreement depends on the use the annotated data will be put to. 1.

Abstract not found

This paper describes an effort to investigate the incrementally deepening development of an interlingua notation, validated by human annotation of texts in English plus six languages. We begin with deep syntactic annotation, and in this paper present a series of annotation manuals for six different languages at the deep-syntactic level of representation. Many syntactic differences between languages are removed in the proposed syntactic annotation, making them useful resources for multilingual NLP projects with semantic components.

This paper describes an interdisciplinary approach which brings together the fields of corpus linguistics and translation studies. It presents ongoing work on the creation of a corpus resource in which translation shifts are explicitly annotated. Translation shifts denote departures from formal correspondence between source and target text, i. e. deviations that have occurred during the translation process. A resource in which such shifts are annotated in a systematic way will make it possible to study those phenomena that need to be addressed if machine translation output is to resemble human translation. The resource described in this paper contains English source texts (parliamentary proceedings) and their German translations. The shift annotation is based on predicate-argument structures and proceeds in two steps: first, predicates and their arguments are annotated monolingually in a straightforward manner. Then, the corresponding English and German predicates and arguments are aligned with each other. Whenever a shift – mainly grammatical or semantic – has occurred, the alignment is tagged accordingly. 1.

This paper presents the main features of an annotation tool, the Coreference Annotator, which manages bilingual corpora consisting of aligned texts that can be grouped in collections and subcollections according to their topics and discourse. The tool allows the manual annotation of certain linguistic items in the source text and their translation equivalent in the target text, by entering useful information about these items based on their context. 1

Abstract not found