From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology
Table 1. The last two columns show the scores of Doc2Vec paragraph vector models trained using different hyper-parameter sets on different corpora. The scores are obtained by: (i) calculating centroids for the 15 hand-coded categories; (ii) selecting the 100 nearest reports for each centroid; (iii) counting the number of incidents (out of 1500) correctly assigned to their centroid.
['[BOLD] Model Parameters  [BOLD] Window Size', '[BOLD] Model Parameters  [BOLD] Minimum Count', '[BOLD] Model Parameters  [BOLD] Subsampling', '[BOLD] Training Corpus  [BOLD] Wikipedia', '[BOLD] Training Corpus  [BOLD] NRLS']
Text Pre-processing and Doc2Vec Model Training. The pre-processing of the raw text consisted of: lowering capital letters; tokenising sentences into words; stemming; and removing punctuation, stop-words and all numeric tokens. To ascertain the effect of the training of the Doc2Vec model on performance, we repeated the training with a broad range of parameters sets (window size, minimum count, subsampling). We carried Doc2Vec training both on a standard (generic, non-specialised) Wikipedia English corpus and on the full NRLS dataset (13+ million records with specialised language). Once optimised, the Doc2Vec model trained on the NRLS corpus is used to infer vectors for each of the N=3229 records in our analysis dataset.