Invariance and identifiability issues for word embeddings
Authors: Rachel Carrington, Karthik Bharath, Simon Preston
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide a formal treatment of the above identifiability issue, present some numerical examples, and discuss possible resolutions. |
| Researcher Affiliation | Academia | Rachel Carrington Karthik Bharath Simon Preston School of Mathematical Sciences, University of Nottingham {rachel.carrington, karthik.bharath, simon.preston}@nottingham.ac.uk |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It references existing models/corpora like GloVe and word2vec, but not its own implementation code. |
| Open Datasets | Yes | The embedding is from model (3), with X taken to be a document term matrix computed from the Corpus of Historical American English [Davies, 2012]... V is a Glo Ve embedding1 with d = 300 trained on Wikipedia 2014 + Gigaword 5 corpus... word2vec embeddings trained on the 100-billion word Google News corpus |
| Dataset Splits | No | The paper mentions using specific test sets but does not provide details on training, validation, or test splits (e.g., percentages or sample counts) for the datasets it uses. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions "R s optim implementation of the Nelder Mead method" but does not specify version numbers for R or the `optim` package. |
| Experiment Setup | No | The paper describes using the Nelder Mead method for optimization and specifies embedding dimensions (e.g., d=300). However, it does not provide concrete hyperparameters (e.g., learning rate, batch size, number of epochs) or specific system-level training settings for its experiments. |