25 research outputs found
A Framework for Interpreting Bridging Anaphora
In this paper we present a novel framework for resolving bridging anaphora.We argue that anaphora, particularly bridging anaphora, is used as a shortcut device similar to the use of compound nouns. Hence, the two natural language usage phenomena would have to be based on the same theoretical framework. We use an existing theory on compound nouns to test its validity for anaphora usages. To do this, we used hu- man annotators to interpret indirect anaphora from naturally occurring discourses. The annotators were asked to classify the relations between anaphor-antecedent pairs into relation types that have been previously used to describe the relations between a modi er and the head noun of a compound noun. We obtained very encouraging results with an average Fleiss's value of 0.66 for inter-annotation agreement. The results were evaluated against other similar natural language interpretation annota- tion experiments and were found to compare well. In order to determine the prevalence of the proposed set of anaphora relations we did a detailed analysis of a subset 20 newspaper articles. The results obtained from this also indicated that a majority (98%) of the relations could be described by the relations in the framework. The results from this analysis also showed the distribution of the relation types in the genre of news paper article discourses
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Ontological Annotation with WordNet
Semantic Web applications require robust and accurate annotation tools that are capable of automating the assignment of ontological classes to words in naturally occurring text (ontological annotation). Most current ontologies do not include rich lexical databases and are therefore not easily integrated with word sense disambiguation algorithms that are needed to automate ontological annotation. WordNet provides a potentially ideal solution to this problem as it offers a highly structured lexical conceptual representation that has been extensively used to develop word sense disambiguation algorithms. However, WordNet has not been designed as an ontology, and while it can be easily turned into one, the result of doing this would present users with serious practical limitations due to the great number of concepts (synonym sets) it contains. Moreover, mapping WordNet to an existing ontology may be difficult and requires substantial labor. We propose to overcome these limitations by developing an analytical platform that (1) provides a WordNet-based ontology offering a manageable and yet comprehensive set of concept classes, (2) leverages the lexical richness of WordNet to give an extensive characterization of concept class in terms of lexical instances, and (3) integrates a class recognition algorithm that automates the assignment of concept classes to words in naturally occurring text. The ensuing framework makes available an ontological annotation platform that can be effectively integrated with intelligence analysis systems to facilitate evidence marshaling and sustain the creation and validation of inference models
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