Semantically Rich Human-Aided Machine Annotation

Proceedings of the Workshop on Frontiers in Corpus Annotation II: Pie in the Sky, pages 68–75, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Semantically Rich Human-Aided Machine Annotation Marjorie McShane, Sergei Nirenburg, Stephen Beale, Thomas O’Hara Department of Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle, Baltimore, Maryland, 21250 USA {marge, sergei, sbeale, tomohara}@umbc.edu Abstract This paper describes a semantically rich, human-aided machine annotation system created within the Ontological Semantics (OntoSem) environment using the DEKADE toolset. In contrast to main- stream annotation efforts, this method of annotation provides more information at a lower cost and, for the most part, shifts the maintenance of consistency to the sys- tem itself. In addition, each tagging effort not only produces knowledge resources for that corpus, but also leads to im- provements in the knowledge environ- ment that will better support subsequent tagging efforts. 1 Introduction Corpus tagging is a prerequisite for many machine learning methods in NLP but has the drawbacks of high cost, inter-annotator inconsistency and the insufficient treatment of meaning. A tagging ap- proach that strives to ameliorate all of these draw- backs is semantically rich, human-aided machine annotation (HAMA), implemented in the OntoSem (Ontological Semantics) environment using a tool- set called DEKADE: the Development, Evaluation, Knowledge Acquisition and Demonstration Envi- ronment of OntoSem. In brief, the OntoSem text analyzer takes as in- put open text and outputs a text-meaning represen- tation (TMR) that represents its meaning using an ontologically grounded, language-independent metalanguage (see Nirenburg and Raskin 2004). Since the processing leading up to the production of TMR includes, in addition to semantic analysis proper, preprocessing (roughly, segmentation, treatment of named entities and morphology) and syntactic analysis, the overall annotation of text in this approach includes tags relating to all of the above levels. Since the typical input for analysis in our practice is genuine sentences, which are on average 25 words long and contain all manner of complex phenomena, it is not uncommon for the automatically generated TMRs to contain errors. These errors—which can occur at the level of pre- processing, syntactic analysis or semantic analy- sis—can be corrected manually using the DEKADE environment, yielding “gold standard” output. Making a human the final arbiter in the process means that such long-term complexities as treatment of metaphor, metonymy, PP-attachment, difficult cases of reference resolution and others can be resolved locally while we work on funda- mental, implementable automatic solutions. In this paper we describe the Onto- Sem/DEKADE environment for the creation of gold standard TMRs, which supports the first ever annotation effort that: • produces structures that can be used as input for both text generators and general reason- ing systems: semantically rich representa- tions of the meaning of text written in a language-independent metalanguage; these representations cover entities, propositions, relations, attributes, speaker attitudes, mo- dalities, polarity, discourse relations, time, reference relations, and more; • produces semantic tagging of text largely automatically, thus making more realistic and affordable the tagging of large amounts of …

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