Summary of the paper

Title Evaluating Hebbian Self-Organizing Memories for Lexical Representation and Access
Authors Claudia Marzi, Marcello Ferro, Claudia Caudai and Vito Pirrelli
Abstract The lexicon is the store of words in long-term memory. Any attempt at modelling lexical competence must take issues of string storage seriously. In the present contribution, we discuss a few desiderata that any biologically-inspired computational model of the mental lexicon has to meet, and detail a multi-task evaluation protocol for their assessment. The proposed protocol is applied to a novel computational architecture for lexical storage and acquisition, the """"Topological Temporal Hebbian SOMs"""" (T2HSOMs), which are grids of topologically organised memory nodes with dedicated sensitivity to time-bound sequences of letters. These maps can provide a rigorous and testable conceptual framework within which to provide a comprehensive, multi-task protocol for testing the performance of Hebbian self-organising memories, and a comprehensive picture of the complex dynamics between lexical processing and the acquisition of morphological structure.
Topics Statistical and machine learning methods, Morphology, Cognitive methods
Full paper Evaluating Hebbian Self-Organizing Memories for Lexical Representation and Access
Bibtex @InProceedings{MARZI12.538,
  author = {Claudia Marzi and Marcello Ferro and Claudia Caudai and Vito Pirrelli},
  title = {Evaluating Hebbian Self-Organizing Memories for Lexical Representation and Access},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
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