Automatically assembling a custom-built training corpus for improving the learning of in-domain word/document embeddings

Abstract

Embedding models turn words/documents into real-number vectors via co-occurrence data from unrelated texts. Crafting domain-specific embeddings from general corpora with limited domain vocabulary is challenging. Existing solutions retrain models on small domain datasets, overlooking potential of gathering rich in-domain texts. We exploit Named Entity Recognition and Doc2Vec for autonomous in-domain corpus creation. Our experiments compare models from general and in-domain corpora, highlighting that domain-specific training attains the best outcome