In natural language understanding, there is a hierarchy of lenses through which we can extract meaning - from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics.
I made an introductory talk on word embeddings in the past and this write-up is an extended version of the part about philosophical ideas behind word vectors.
Longer documents, such as those with several chapters or tables and figures, must follow a consistent and standard format. These PDFs describe how to do this in Microsoft Word.
Choose two documents (Word, PDF, PowerPoint Files). See the changes side-by-side.
Für vertrauliche Dokumente gibt es eine (kostenpflichtige) Offline-Version zum Download.
ConceptNet Numberbatch consists of state-of-the-art semantic vectors (also known as word embeddings) that can be used directly as a representation of word meanings or as a starting point for further machine learning.
M. Kusner, Y. Sun, N. Kolkin, and K. Weinberger. Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37, page 957--966. JMLR.org, (2015)