The recent growth of black-box machine-learning methods in data analysis has increased the demand for explanation methods and tools to understand their behaviour and assist human-ML model cooperation.
Then, we train several embedding models on a text corpus and select the best model, that is, the model that maximizes the correlation between the HSS and the cosine similarity of the pair of words that are in both the taxonomy and the corpus.
The need for explanations of ML systems is growing as new models outperform their predecessors while becoming more complex and less comprehensible for their end-users.
The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors.
Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction.