The (Non-)Sense of Word Vectors (2/2)
This is the second part in a two-series blog. Read the first part here.
In the previous part, we discussed the differences between syntactical and semantical applications of natural language processing. In both scenarios, word vectors seem like a promising way forward given their recent popularity and success. As we outlined, word vectors work quite well on syntactical tasks given that the nature of word vectors comes from looking at words’ surroundings. For semantical applications however, we have argued that they are less suitable.
We were mainly looking to apply word vectors on sentiment analysis as a first semantical application. Our award-winning and patented RBEM algorithm is great on paper but has its flaws as it requires a vast amount of human expert input to work correctly, something we were hoping to overcome using word vectors.