Semantically Coherent Vector Space Representations
|Type||Appeared in Conference without Proceedings|
|MU Faculty or unit|
Our work is a scientific poster that was presented at the ML Prague 2019 conference during February 22–24, 2019.
Content is king (Gates, 1996). Decomposition of word semantics matters (Mikolov, 2013). Decomposition of a sentence, paragraph, and document semantics into semantically coherent vector space representations matters, too. Interpretability of these learned vector spaces is the holy grail of natural language processing today, as it would allow accurate representation of thoughts and plugging-in inference into the game.
We will show recent results of our attempts towards this goal by showing how decomposition of document semantics could improve the query answering, performance, and “horizontal transfer learning” based on word2bits could be achieved.
Word representation in the form of binary features allows to use word lattice representation for feature inference by the well studied formal concept analysis theory, and for precise semantic similarity metric based on discriminative features. Also, the incremental learning of word features allows to interpret and infer on them, targeting the holy grail.