Browse free open source Java Linguistics Software and projects below. Use the toggles on the left to filter open source Java Linguistics Software by OS, license, language, programming language, and project status.

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  • 1
    DArtikel!

    DArtikel!

    Learn the articles of German words.

    Learn words in german that you know at your own pace. With this system you can add the words you knew in a day and then do exercises with them. Written by: Jovanny Pablo Cruz Gómez. Computer Engineering Student. IPN, ESIME Culhuacan, Mexico City.
    Downloads: 0 This Week
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  • 2
    Thinknowlogy

    Thinknowlogy

    The world's only naturally intelligent knowledge technology

    Natural intelligence is the utilization of naturally occurring logic. This naturally occurring logic provides concrete clues for organizing natural objects, like: - Grouping objects that belong together, - Separating objects that don't belong together, - Archiving objects that have become less important. Natural language and spatial information are sources of natural intelligence: - Natural language is providing concrete logic for organizing knowledge objects, - Spatial information provides concrete logic for organizing spatial objects (utilized in, e.g., self-driving cars). In this way, our brains know how to organize their knowledge and spatial information. I focus on natural language because this source of natural intelligence is hardly understood by scientists. Hence, the inability of Large Language Models to organize changes in their knowledge independently.
    Downloads: 0 This Week
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  • 3
    Unsupervised TXT classifier

    Unsupervised TXT classifier

    Classify any two TXT documents, no training required - JAVA

    This program is made to address two most common issues with the known classifying algorithms. First, over-training and second, shortage of data for a training of categories. Instead, each TXT file is a category on its own, rather than an assigned category. In a way, this is similar to clustering but not really a clustering algorithm since there is some training involved. The summarizer from Classifier4J has been adjusted to accept two inputs (lets call them A and B). Then, the summarizer gets trained with A to summarize a document B, and vice versa. This extracts a relevant structure for both documents (and thus avoids the over-training) which are then compared using the Vector-Space analysis to give a range of belonging of one document to another (and thus avoids the shortage of information). This method can be used to create the user-defined classes by merging texts of certain categories and then to calculate the relevant distances between the documents, but this is not necessary.
    Downloads: 0 This Week
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