Eleni Zimianiti

Publications

Displaying 1 - 3 of 3
  • Zimianiti, E. (2022). Is semantic memory the winning component in second language teaching with Accelerative Integrated Method (AIM)? LingUU Journal, 6(1), 54-62.

    Abstract

    This paper constitutes a research proposal based on Rousse-Malpalt’s
    (2019) dissertation, which extensively examines the effectiveness of the
    Accelerative Integrated Method (AIM) in second language (L2) learning.
    Although it has been found that AIM is a greatly effective method in comparison with non-implicit teaching methods, the reasons behind its success and effectiveness are yet unknown. As Semantic Memory (SM) is the component of memory responsible for the conceptualization and storage of knowledge, this paper sets to propose an investigation of its role in the learning process of AIM and provide with insights as to why the embodied experience of learning with AIM is more effective than others. The tasks proposed for administration take into account the factors of gestures being related to a learner’s memorization process and Semantic Memory. Lastly, this paper provides with a future research idea about the learning mechanisms of sign languages in people with hearing deficits and healthy population, aiming to indicate which brain mechanisms benefit from the teaching method of AIM and reveal important brain functions for SLA via AIM.
  • Zimianiti, E. (2020). Verb production and comprehension in dementia: A verb argument structure approach. Master Thesis, Aristotle University of Thessaloniki, Thessaloniki, Greece.

    Abstract

    The purpose of this study is to shed light to the linguistic deficit in populations with dementia, and more specifically with Mild Cognitive Impairment and Alzheimer’s Disease; by examining the assignment of thematic roles (θ-roles) in sentences including psychological verbs.
    The interest in types of dementia and its precursor is due to the relevance of the disease in present-day world society (Caloi, 2017). 47 millions of people worldwide were reported by the World Alzheimer Report in 2016 (Prince et al. 2016) as people with a type of dementia. This number surpasses the number of inhabitants in Spain, a whole country, and it is expected, according to the report, to triplicate until 2050 reaching the number of 131 million. The impact of this disease is observed not only at the social level but also in the economic one, because of their need for assistance in their everyday life. What is worrying, is the lack of total treatment once the disease has started. Despite the efforts of medicine, dementia is problematic in terms of its diagnosis, because a variety of cognitive abilities is assessed in combination with medical workup. Language is a crucial component in the procedure of diagnosis as linguistic deficits are among the first symptoms that accompany the onset of the disease. Therefore, further investigation of linguistic impairment is a necessity in order to enhance the diagnostic techniques used nowadays. Furthermore, the lack of efficient drugs for the treatment of the disease has necessitated the development of training programs for maintenance and increase of the cognitive abilities in people with either Mild Cognitive Impairment or a type of dementia …
  • 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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