Eleni Zimianiti

Publications

Displaying 1 - 2 of 2
  • Zimianiti, E., Dimitrakopoulou, M., & Tsangalidis, A. (2021). Τhematic roles in dementia: The case of psychological verbs. In A. Botinis (Ed.), ExLing 2021: Proceedings of the 12th International Conference of Experimental Linguistics (pp. 269-272). Athens, Greece: ExLing Society.

    Abstract

    This study investigates the difficulty of people with Mild Cognitive Impairment (MCI), mild and moderate Alzheimer’s disease (AD) in the production and comprehension of psychological verbs, as thematic realization may involve both the canonical and non-canonical realization of arguments. More specifically, we aim to examine whether there is a deficit in the mapping of syntactic and semantic representations in psych-predicates regarding Greek-speaking individuals with MCI and AD, and whether the linguistic abilities associated with θ-role assignment decrease as the disease progresses. Moreover, given the decline of cognitive abilities in people with MCI and AD, we explore the effects of components of memory (Semantic, Episodic, and Working Memory) on the assignment of thematic roles in constructions with psychological verbs.
  • Anastasopoulos, A., Lekakou, M., Quer, J., Zimianiti, E., DeBenedetto, J., & Chiang, D. (2018). Part-of-speech tagging on an endangered language: a parallel Griko-Italian Resource. In Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018) (pp. 2529-2539).

    Abstract

    Most work on part-of-speech (POS) tagging is focused on high resource languages, or examines low-resource and active learning settings through simulated studies. We evaluate POS tagging techniques on an actual endangered language, Griko. We present a resource that contains 114 narratives in Griko, along with sentence-level translations in Italian, and provides gold annotations for the test set. Based on a previously collected small corpus, we investigate several traditional methods, as well as methods that take advantage of monolingual data or project cross-lingual POS tags. We show that the combination of a semi-supervised method with cross-lingual transfer is more appropriate for this extremely challenging setting, with the best tagger achieving an accuracy of 72.9%. With an applied active learning scheme, which we use to collect sentence-level annotations over the test set, we achieve improvements of more than 21 percentage points

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