Finding the Neural Net: Deep-learning Idiom Type Identification from Distributional Vectors

The present work aims at automatically classifying Italian idiomatic and non-idiomatic phrases with a neural network model under constrains of data scarcity. Results are discussed in comparison with an existing unsupervised model devised for idiom type detection and a similar supervised classifier previously trained to detect metaphorical bigrams. The experiments suggest that the distributional context of a given phrase is sufficient to carry out idiom type identification to a satisfactory degree, with an increase in performance when input phrases are filtered according to human-elicited idiomaticity ratings collected for the same expressions. Crucially, employing concatenations of single word vectors rather than whole-phrase vectors as training input results in the worst performance for our models, differently from what was previously registered in metaphor detection tasks

Panta rei: Tracking Semantic Change with Distributional Semantics in Ancient Greek

We present a method to explore semantic change as a function of variation in distributional semantic spaces. In this paper, we apply this approach to automatically identify the areas of semantic change in the lexicon of Ancient Greek between the pre-Christian and Christian era. Distributional Semantic Models are used to identify meaningful clusters and patterns of semantic shift within a set of target words, defined through a purely data-driven approach. The results emphasize the role played by the diffusion of Christianity and by technical languages in determining semantic change in Ancient Greek and show the potentialities of distributional models in diachronic semantics

Comparative Proteomic Analysis of Serum from Patients with Systemic Sclerosis and Sclerodermatous GVHD. Evidence of Defective Function of Factor H

BACKGROUND: Systemic sclerosis (SSc) is an autoimmune disease characterized by immunological and vascular abnormalities. Until now, the cause of SSc remains unclear. Sclerodermatous graft-versus-host disease (ScGVHD) is one of the most severe complications following bone marrow transplantation (BMT) for haematological disorders. Since the first cases, the similarity of ScGVHD to SSc has been reported. However, both diseases could have different etiopathogeneses. The objective of this study was to identify new serum biomarkers involved in SSc and ScGVHD. METHODOLOGY: Serum was obtained from patients with SSc and ScGVHD, patients without ScGVHD who received BMT for haematological disorders and healthy controls. Bi-dimensional electrophoresis (2D) was carried out to generate maps of serum proteins from patients and controls. The 2D maps underwent image analysis and differently expressed proteins were identified. Immuno-blot analysis and ELISA assay were used to validate the proteomic data. Hemolytic assay with sheep erythrocytes was performed to evaluate the capacity of Factor H (FH) to control complement activation on the cellular surface. FH binding to endothelial cells (ECs) was also analysed in order to assess possible dysfunctions of this protein. PRINCIPAL FINDINGS: Fourteen differentially expressed proteins were identified. We detected pneumococcal antibody cross-reacting with double stranded DNA in serum of all bone marrow transplanted patients with ScGVHD. We documented higher levels of FH in serum of SSc and ScGVHD patients compared healthy controls and increased sheep erythrocytes lysis after incubation with serum of diffuse SSc patients. In addition, we observed that FH binding to ECs was reduced when we used serum from these patients. CONCLUSIONS: The comparative proteomic analysis of serum from SSc and ScGVHD patients highlighted proteins involved in either promoting or maintaining an inflammatory state. We also found a defective function of Factor H, possibly associated with ECs damage

Scratching your tête over code-switched idioms: Evidence from eye movement measures of reading (M. Senaldi, J. Wei, J. Gullifer, D. Titone)

This is the OSF page of AMLaP 2020 poster #289. This work was co-authored by Junyan Wei (Nanjing University of Chinese Medicine), Jason Gullifer (McGill University) and Debra Titone (McGill University)

Scratching your tête over code-switched idioms: Evidence from eye movement measures of reading (M. Senaldi, J. Wei, J. Gullifer, D. Titone)

This is the OSF page of AMLaP 2020 poster #289. This work was co-authored by Junyan Wei (Nanjing University of Chinese Medicine), Jason Gullifer (McGill University) and Debra Titone (McGill University)