Resolving Individual and Abstract Anaphora in Texts and Dialogues

Resolving Individual and Abstract Anaphora in Texts and Dialogues Costanza Navarretta Center for Sprogteknologi, University of Copenhagen Njalsgade 80 2300 Copenhagen S costanza@cst.dk Abstract This paper describes an extension of the dar-algorithm (Navarretta, 2004) for re- solving intersentential pronominal anaphors referring to individual and abstract enti- ties in texts and dialogues. In dar indi- vidual entities are resolved combining mod- els which identify high degree of salience with high degree of givenness (topicality) of entities in the hearer’s cognitive model, e.g. (Grosz et al., 1995), with Hajiˇcov´a et al.’s (1990) salience account which assigns the highest degree of salience to entities in the focal part of an utterance in Infor- mation Structure terms, which often intro- duce new information in discourse. Anaph- ors referring to abstract entities are resolved with an extension of the algorithm pre- sented by Eckert and Strube (2000). The extended dar-algorithm accounts for differ- ences in the resolution mechanisms of differ- ent types of Danish pronouns. Manual tests of the algorithm show that dar performs better than other resolution algorithms on the same data. 1 Introduction Although most pronominal anaphora resolu- tion algorithms only account for anaphors re- ferring to individual entities, anaphors re- ferring to abstract entities evoked by verbal phrases, clauses or discourse segments (hence- forth APAs) are quite common in English see i.a. (Byron and Allen, 1998) and even more in Danish (Navarretta, 2004). Recently, two algorithms for resolving APAs and individ- ual pronominal anaphors (henceforth IPAs) in specific English dialogues have been proposed Eckert and Strube’s (2000) es00 and Byron’s (2002) phora. Furthermore Strube and M¨uller (Strube and M¨uller, 2003) have presented a machine learning approach for resolving APAs in spoken dialogues.1 Both es00 and phora 1We do not discuss this approach further in this pa- per. recognise IPAs and APAs on the basis of se- mantic constraints on the argument position oc- cupied by the anaphors and account for differ- ences in reference between personal and demon- strative pronouns. In es00 demonstrative pro- nouns preferentially refer to abstract entities, while personal pronouns preferentially refer to individual ones. es00 resolves IPAs applying Strube’s (1998) algorithm. In phora the an- tecedents of personal pronouns are searched for looking at their degree of salience which is im- plemented by word order as in (Grosz et al., 1995). Demonstratives, instead, are searched for in the list of activated entities (Gundel et al., 1993) containing non NP antecedents, which are assumed to be less salient. In phora demon- stratives can also refer to Kinds. es00 requires that the structure of dialogues has been marked. Byron’s phora-algorithm does not rely on predefined dialogue structure, but only searches for abstract antecedents of APAs in the sentence preceding the anaphor. Thus it does not account for APAs referring to larger discourse segments. phora relies on both semantic knowledge and a model of speech acts and accounts for more phenomena than es00. Differing from es00, phora has been imple- mented. To resolve IPAs and APAs in Danish texts and dialogues …

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