We want to build a GNN-based edge prediction BOW model for SDI. We hypothesize that it has a higher performance than the simple baseline model.
Motivation: SDI with F1 > 0.30 for 1 tpi/meu
Tasks
[ ] Acquire refined mappings from verses to semantic domains #1
[ ] use refined mappings from words in verses to SDs to assign SDs to words in verses from LRL
simply assign SDs in eng to each aligned word in LRL
if many false positive mappings (i.e., low precision): refine assignments with generated SD dicts for LRL (set intersection)
[ ] collect BOW for every word with assigned SD (2 words before and after word in the middle)
[ ] aggregate BOWs by SD
[ ] perform SDI by extracting BOW for every candidate word in input sentence and compute cosine dist to aggregated BOW
[ ] try out baseline: look up each word in a dictionary
[ ] consider usefulness of WSD (word sense disambiguation) with pywsd or different tool: Eng verse → WordNet → SD (see Jonathan’s 2nd mail)
Goal
We want to build a
GNN-based edge predictionBOW model for SDI. We hypothesize that it has a higher performance than the simple baseline model. Motivation: SDI with F1 > 0.30 for 1 tpi/meuTasks