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In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
reStructuredText (reSt) - plaintext platform-independent markup used as Python libraries documentation tool, multiple output formats (HTML, LaTeX, ODT, EPUB, ...) [12] Retail Template Markup Language (RTML) – e-commerce language which is based on Lisp.
The space of documents is then scanned using HDBSCAN, [20] and clusters of similar documents are found. Next, the centroid of documents identified in a cluster is considered to be that cluster's topic vector. Finally, top2vec searches the semantic space for word embeddings located near to the topic vector to ascertain the 'meaning' of the topic ...
What follows is a list of interfaces, grouped by the object that usually needs to implement them. Interfaces usually implemented by the OLE object are usually called on by the OLE container, and vice versa. Note that in the following list indentation indicates interface inheritance. All non-indented interfaces derive from IUnknown.
The Document Theme defines the colors, fonts and graphic effects for a document. Almost everything that can be inserted into a document is automatically styled to match the overall document theme creating a consistent document design. The new Office Theme file format (.THMX) is shared between Word, Excel, PowerPoint and Outlook email messages.
The festivities aren’t limited to Thanksgiving Day. For an entire week leading up to the game, fans visiting the on-site 1919 Kitchen & Tap, which is open at the stadium year-round, can order a ...
By default, AOL Mail shows the count of unread emails in each folder. To view the total number of emails, hover your mouse over the folder for a few seconds.
An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). [9] However, more elaborate solutions based on word vector quantization have also been proposed.