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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]
Font embedding is the inclusion of font files inside an electronic document for display across different platforms. Font embedding is controversial because it allows licensed fonts to be freely distributed.
Text in PDF is represented by text elements in page content streams. A text element specifies that characters should be drawn at certain positions. The characters are specified using the encoding of a selected font resource. A font object in PDF is a description of a digital typeface.
Embedding vectors created using the Word2vec algorithm have some advantages compared to earlier algorithms [1] such as those using n-grams and latent semantic analysis. GloVe was developed by a team at Stanford specifically as a competitor, and the original paper noted multiple improvements of GloVe over word2vec. [ 9 ]
pdfdetach – extract embedded documents from a PDF; pdffonts – lists the fonts used in a PDF; pdfimages – extract all embedded images at native resolution from a PDF; pdfinfo – list all information of a PDF; pdfseparate – extract single pages from a PDF; pdftocairo – convert single pages from a PDF to vector or bitmap formats using ...
PDF is a standard for encoding documents in an "as printed" form that is portable between systems. However, the suitability of a PDF file for archival preservation depends on options chosen when the PDF is created: most notably, whether to embed the necessary fonts for rendering the document; whether to use encryption; and whether to preserve additional information from the original document ...
Text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. [1] At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens ...
In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance [8] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.