Marco S. G. Senaldi

Also published as: Marco S.G. Senaldi


Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here


2023

pdf bib
Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language Models
Giulia Rambelli | Emmanuele Chersoni | Marco S. G. Senaldi | Philippe Blache | Alessandro Lenci
Proceedings of the 19th Workshop on Multiword Expressions (MWE 2023)

An open question in language comprehension studies is whether non-compositional multiword expressions like idioms and compositional-but-frequent word sequences are processed differently. Are the latter constructed online, or are instead directly retrieved from the lexicon, with a degree of entrenchment depending on their frequency? In this paper, we address this question with two different methodologies. First, we set up a self-paced reading experiment comparing human reading times for idioms and both highfrequency and low-frequency compositional word sequences. Then, we ran the same experiment using the Surprisal metrics computed with Neural Language Models (NLMs). Our results provide evidence that idiomatic and high-frequency compositional expressions are processed similarly by both humans and NLMs. Additional experiments were run to test the possible factors that could affect the NLMs’ performance.

2017

pdf bib
Deep-learning the Ropes: Modeling Idiomaticity with Neural Networks
Yuri Bizzoni | Marco S.G. Senaldi | Alessandro Lenci
Proceedings of the Fourth Italian Conference on Computational Linguistics (CLiC-it 2017)