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RQ: What is the best way of introducing domain-specific knowledge into a general word embedding model, in order to produce high quality embeddings within that domain? How can we balance the information provided by the domain-specific corpus and the generic knowledge? After enriching the embeddings, how can we effectively measure the domain adaptation?
RQ: How can we combine the spherical word and document embedding knowledge to create Topic Models ? Would there be a fundamental difference between an approach as such to the statistical topic modeling, e.g. LDA? What additional information can we learn from embedding topics to the word- and document-vector space? Would it be possible to analytically describe topic vectors mathematically for a more efficient and general application?
RQ: How do we best complete the definitions in a prototype ontology by enriching each defined entity with its position in the semantic space? How do we best represent an existing domain specific ontology in a semantic space in order to detect the appearance of its entities in text? By identifying their semantic properties through word embeddings, what is the best way to integrate these properties into the ontology?