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2. List of PDF software - Wikipedia

   en.wikipedia.org/wiki/List_of_PDF_software

   Free ( Mozilla Public License) an Office suite; allows to export (and import, with accuracy limitations) PDF files. Microsoft Word 2013. Proprietary. Desktop software. The 2013 edition of Office allows PDF files to be converted into a format that can be edited. Nitro PDF Reader. Trialware.

3. Solid Converter PDF - Wikipedia

   en.wikipedia.org/wiki/Solid_Converter_PDF

   Solid Converter PDF. Solid Converter PDF is document reconstruction software from Solid Documents which converts PDF files to editable formats. Originally released for the Microsoft Windows operating system, a Mac OS X version was released in 2010. The current versions are Solid Converter PDF 9.0 for Windows and Solid PDF to Word for Mac 2.1.

4. Antiword - Wikipedia

   en.wikipedia.org/wiki/Antiword

   Antiword. Antiword is a free software reader for proprietary Microsoft Word documents, and is available for most computer platforms. Antiword can convert the documents from Microsoft Word version 2, 6, 7, 97, 2000, 2002 and 2003 to plain text, PostScript, PDF, and XML / DocBook (experimental).

5. Help:WordToWiki - Wikipedia

   en.wikipedia.org/wiki/Help:WordToWiki

   Download the "Microsoft Office Word Add-in For MediaWiki" from Microsoft Download Center, and install it. Save the document as "MediaWiki (*.txt)" file type. Copy the text from the (*.txt) file into your Wiki page; Note that this extension does not work for Word 2013 by default, however it can be made to work with a registry change. See this page.

6. List of word processors - Wikipedia

   en.wikipedia.org/wiki/List_of_word_processors

   DavkaWriter – available for macOS and Windows. Final Draft – screenplay/teleplay word processor, available for macOS and Windows. Adobe FrameMaker – Windows. Gobe Productive Word Processor – Windows and Linux. Google Docs. Hangul (also known as HWP) – Windows, Mac and Linux. IA Writer – Mac, iOS. IBM SCRIPT – IBM VM/370.

7. Word2vec - Wikipedia

   en.wikipedia.org/wiki/Word2vec

   The word with embeddings most similar to the topic vector might be assigned as the topic's title, whereas far away word embeddings may be considered unrelated. As opposed to other topic models such as LDA , top2vec provides canonical ‘distance’ metrics between two topics, or between a topic and another embeddings (word, document, or otherwise).