Corien Bary

The Dynamics of Tense under Attitudes --- Anaphoricity and de se Interpretation in the Backward Shifted Past

This paper shows that both anaphoricity and egocentric de se binding play a crucial role in the interpretation of tense in discourse. It uses the English backwards shifted reading of the past tense in a mistaken time scenario to bring out the tension between these two features. We provide a suitable representational framework for the observed clash in the form of an extension of DRT in which updates of the common ground are accompanied by updates of each relevant agents complex attitudinal state.

Tense in Ancient Greek Reports

This paper shows that tenses in Ancient Greek reports have both an anaphoric and a shifted-indexical component. It develops a new account of Ancient Greek tenses that deals with this combination of features. Following Kleins (1994) semantics of tense, I claim that tense indicates the relation between an anaphoric topic time and the moment of utterance, the now. As for Ancient Greek indirect discourse, I argue that the anaphoric component works exactly the same as in non-embedded discourse (contra von Stechow 1995). The indexical component, however, is di ferent in that the now is not provided by the actual context of utterance, but by the reported context. The DRT account developed models this combination of features, taking also into account the role of aspect.

A Memory-Based Lemmatizer for Ancient Greek

In this paper we present the lemmatizer that we developed for Ancient Greek: GLEM. As far as we know, GLEM is the first publicly available lemmatizer for Ancient Greek that uses POS information to disambiguate and that also assigns output to unseen words, words that are not yet in the lexicon. As the basis for the lemmatizer we used an existing memory-based learning tool, Frog, that was originally developed for Dutch and that we converted to work for Ancient Greek. As the results of Frog on Ancient Greek were rather modest, we used Frog to create a smarter lemmatizer, GLEM, that uses a lexicon look up in addition to the memory-based tool Frog. We evaluate and compare the performance of GLEM against the Frog lemmatizer and the already existing CLTK lemmatizer and observe that GLEM achieves the highest accuracy of 93% on an unseen test corpus sample. GLEMs look up component overcomes the difficulty of a relative small training set in combination with a morphologically rich language, while the memory-based learning component enables GLEM to handle unknown words.