Jumper provides an enterprise bookmarking engine for tagging and linking data objects. It lets you search and share high-value data across remote locations using tag metadata (expanded tag fields) to capture knowledge about data in remote data stores. It collects these tag profiles in a knowledge base where user-created tag profiles identify quality data resources, user-contributed tag information adds real-world knowledge about the data resources, and user-created reviews sort out the worthy resources from the inadequate. Other users can search for this data. In addition, they can directly contribute what they know about this data to the knowledge base. It allows the participants to act as a filter for what is valuable and build upon mainstream pursuits, but also uncovers valuable data hidden at the edge.
MP3 Diags finds problems in MP3 files and helps the user fix many of them. It looks at both the audio part (VBR info, quality, normalization) and the tags containing track information (ID3). It has a tag editor, which can download album information (including cover art) from MusicBrainz and Discogs, as well as paste data from the clipboard. Track information can also be extracted from a file's name. Another component is the file renamer, which can rename files based on the fields in their ID3V2 tag (artist, track number, album, genre, etc.).
PhotoCatalog can import GPS data from various formats such as CSV, GPX, and InstaMapper (in the case of InstaMapper, it is a live stream) and create a Map that is updated live during your travels. It can also import data from Twitter or FourSquare, RSS extract any GPS data they have or supplement them with existing GPS data, and place them on a map as well as keeping a personal archive. It can then import photos via file upload, email, or scanning an existing folder, geotag photos lacking GPS data, sort, rotate, scale, and [losslessly] compress them as necessary and place them on a map. It can also push them to Facebook automatically and fill in a caption and/or comment with them.
Apache OpenNLP is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services.