Integrated Linguistic Annotation Models and Their Application in the Domain of Antecedent Detection

Abstract

Abstract. Seamless integration of various, often heterogeneous linguistic resources in terms of their output formats and a combined analysis of the respective annotation layers are crucial tasks for linguistic research. After a decade of concentration on the development of formats to structure single annotations for specific linguistic issues, in the last years a variety of specifications to store multiple annotations over the same primary data has been developed. The paper focuses on the integration of the knowledge resource logical document structure information into a text document to enhance the task of automatic anaphora resolution both for the task of candidate detection and antecedent selection. The paper investigates data structures necessary for knowledge integration and retrieval. IntroductionAnaphora Resolution (AR) describes the process of identifying the correct antecedent for a given anaphoric element and, in general, consists of three steps: (1) identification of anaphoric elements, (2) creation of a candidate set for each anaphora and (3) detection of the correct antecedent from the candidate set. In this paper we will focus on the second and third step and we will investigate the question how to create an appropriate candidate set.In recent approaches that define anaphora resolution as a pairwise decision, the candidate set is created by choosing all candidates that precede a given anaphora