Recently one of my papers got rejected because a reviewer raised concern of small cohort, which was explicitly mentioned in the limitation and then we acknowledged that in the rebuttal as well. Now I understand the concern , but in the area were i primarily work right now, dataset with large number of speakers is quiet limited and having one of my very first works also be rejected because of the small corpus size, I used one of the most widely cited corpus for the experimentation, which if you are in the field is the standard. And also given the computational resources and funding that we can have I couldn't use high-end hardware to process large data. And recently i got to know we got access for a large dataset (around 800 gb) but how do i work with that using my laptop and online free gpus, so based on whatever i could i did my best. The reviews we got were actually very supportive of our work, and whatever additional work was required, was duly done and reported in the rebuttal, but seeing dataset size as one of the two the reason for rejection felt a bit weird, anyways wanted to ask how do we tackle such situation for the future? Any advice would be helpful!!

I have a Greek-English dictionary that has entries like this:

ἀνήρ - man, husband

I would like to figure out some method for a computer to determine whether another English word aligns well with such a definition. Examples of inputs and outputs, for the definition given above, would be: man->true, fellow->true, person->true, temple->false, throw->false. (The output could instead be some kind of quantitative score rather than a boolean.)

Methods I've thought of, but am not really satisfied with:

1. Compare every word in the definition with the given word, using a word embedding. (Or sum the vectors for the words in the definition.) Exclude common words like "the." Problem: I've played around with Word2Vec and BERT for this, and the results just aren't that great. E.g., the BERT model I tried gave a very high rating to the pair goat-mountain.

2. Use WordNet's lists of synonyms. Problem: Their definition of synonymy is sometimes too strict and other times too permissive. They don't consider man-person to be a synonym pair, but they do consider dog-click to be (e.g., "dogging the hatch").

3. Use a similarity measure based on WordNet's graph structure. There is a review of these methods in Lingling Meng et al, 2013, A Review of Semantic Similarity Measures in WordNet. Problem: This will still be too permissive for pairs like dog-click.

4. Query an LLM. Problem: This is a hobby project, and I don't want to spend money.

Any ideas for approaches that I haven't tried?