TY - CONF
ID - 1525379
AU - Sojka, Petr - Novotný, Vít
PY - 2019
TI - Semantically Coherent Vector Space Representations
KW - artificial intelligence
KW - machine learning
KW - computational linguistics
KW - information retrieval
KW - representation learning
KW - word embeddings
KW - formal concept analysis
KW - transfer learning
KW - word2vec
KW - word2bits
UR - https://mir.fi.muni.cz/ml-prague-2019/#semantic-representations
N2 -
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.
ER -
SOJKA, Petr a Vít NOVOTNÝ. \textit{Semantically Coherent Vector Space Representations}. 2019.
