Java Linguistics Software

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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
    Sanchay
    Sanchay is a collection of tools and APIs for language researchers. It has some implementations of NLP algorithms, some flexible APIs, several user friendly annotation interfaces and Sanchay Query Language for language resources.
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  • 2
    Standardizing the existing RelEx2Frame Engine of the RelEx semantic dependency relationship extractor and adding an statistical learning AI for automatic extension of the rule base
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  • 3

    Semantic Assistants

    Natural Language Processing (NLP) for the Masses

    Semantic Assistants support users in content retrieval, analysis, and development, by offering context-sensitive NLP services directly integrated in standard desktop clients, like a word processor, and web information systems, like a wiki.
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  • 4
    Java implementation of semirings as used in Joshua Goodman's thesis. Allows parsers to easily return different kinds of values by simply changing the semiring.
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  • 5

    SimpleLemmatizer

    This program is for text lemmatization

    It lemmatizes texts based on supplied model. The base model is for slovak texts and is created from Slovak National Corpus, copyright by Ľ. Štúr Institute of Linguistics, Slovak Academy of Sciences
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  • 6
    A simple Java GUI tool for looking at the Spectrum and Cepstrum of a sound clip.
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  • 7
    A Java application that tries to learn the ontology of sport articles in German newspapers.
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  • 8
    Stemmer Gujarati

    Stemmer Gujarati

    Offline stemmer for Gujarati , which is one of 22 Indian languages.

    This is a Gujarati stemmer in Java. Stemming is a process in which affixes are removed form the root word (stem). It relates morphological variant words to corresponding common root. For example "પ્રતિઉપયોગી" is word which has stem " ઉપયોગ". Stemmers are language specific tools. The design of a stemming algorithm requires a significant level of linguistic expertise. There has been lot of significant work in the development and evaluation of stemmer for non-Indian languages, but very less or no significant work has been done on Indian front especially for Gujarati language.The code of this stemmer is based on algorithm designed under guidance of Prof. Nikita Desai, India. It takes input file of type .txt containing Gujarati text encoded as UTF-8 and then removes stop words which are unessential. After processing rest of the words, it outputs corresponding file containing all stem words plus other details.
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  • 9

    Supertagger

    Software for assigning supertags.

    Supertagging is a process of statistical lexical disambiguation, preprocessing step to parsing, which assigns LTAG tree categories to the lexical items present in the input sentence. Thus, if the input sentence is in the form of a dependency tree, the task of the supertagger is to assign the most probable TAG family to each node and edge in the dependency tree.
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  • 10
    SweetOnionCCG2PTBConverter

    SweetOnionCCG2PTBConverter

    A tool that converts CCGBank to PTB

    Conversion between different grammar frameworks is of great importance to comparative performance analysis of the parsers developed on them. This tool can convert CCG derivations to PTB trees by using Max Entropy models as well as visualizing the tree graphs. The main technical innovation presented here is the effective conversion method which achieves a F score over 95%.
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  • 11
    TF-IDF.jar is a Java Archive file to measure TF-IDF of each document in a document collection (corpus). The jar can be used to (a) get all the terms in the corpus (b) get the document frequency (DF) and inverse document frequency (IDF) of all the terms in the corpus (c) get the TF-IDF of each document in the corpus (d) get each term with their frequency (no. of presence), term frequency (TF) and TF-IDF in every document
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  • 12
    TIES

    TIES

    A smart search engine for medical documents

    TIES (Text Information Extraction System) is a clinical text search engine that uses Natural Language Processing techniques to extract medical concepts from free text clinical reports. It provides secure de-identified access to this information and has in built collaboration tools and honest broker functionality. It is licensed for academic use under the BSD license. For commercial use please contact Nexi at http://nexihub.com *** NOTICE: this software and forum are no longer maintained, as of 8/15/2019. You are free to continue to use this software under the license for academic use under the BSD license. For commercial use please contact Nexi at http://nexihub.com
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  • 13

    TML - Text Mining Library for LSA & CMM

    TML is a Java Library for LSA and extracting Concept Maps from text

    TML has moved to http://www.villalon.cl/tml.html and the code to https://github.com/villalon/tml
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  • 14
    Set of tools designed to edit, check and mantain TMX translation memories.
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  • 15

    Texalyzer

    Text analyzer

    Analyzes text document using TF-IDF and optionally stopword list, and extracts important keywords.
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  • 16
    Text Expander, Inverse summarizer

    Text Expander, Inverse summarizer

    Expand text, inverse summarizer

    IT WILL WORK WITH A JAVA DEVELOPMENT KIT 1.7 ONLY !!! This program is a data-miner and a knowledge-miner. It does exactly the opposite of what the text summarizers do. A text summarizer produces a shortened text given some text as an input. An inverse summarizer takes the shortened input, a similar or a same text and does the process in reverse. This results in an expanded text. It can be used with any text or notes that have the knowledge gaps. It is a great aid to any creative work and it simply pin-points to data that may be of some relevance. How to run this program? 1. Make sure JAVA 1.7 development is installed and running/compiling properly with all environment variables properly set. 2. Uncompress the LITE release into a desired directory. 3. Go to a src/ directory with a Terminal/Console/Command-Prompt and do the following: javac Te2.java java Te2 The program should open.
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  • 17

    TextComparer

    Small Java program to compare two texts

    Small Java program to compare two texts, originally designed to be used to find quotations in a Byzantine anthology. It can quite likely be used to detect plagiarism between two texts as well Graphical interface which allows easy navigation between corresponding parts in the two different texts. Uses the http://software.jessies.org/salma-hayek/ Java TextArea for this. Probably very much can be done to improve this program and the algorithm which it uses.
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  • 18
    Linear text segmentation algorithm based on a sliding window technique.
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  • 19

    TextualModelGenerator

    Generator for textual models by applying different techniques

    This is a project created and supported by: Angel Castellanos Juan Cigarrán Recuero Ana García Serrano This projects allows the modelling of textual contents by applying different techniques: TF-IDF KLD Mutual Information Chi^2 With this application the users can be able to extract the most representative terminology of a textual collection. The application is Java-based, allowing their execution in several platforms and operative systems (Windows, Linux, MacOS).
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  • 20
    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.
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  • 21
    This project is a compilation of tools/libraries to help with tasks related to Text Analytics mainly in Java. These tools range from simple wrappers to sophisticated mining tasks that can improve the productivity of researchers and engineers.
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  • 22
    Tradubi is an open-source Ajax-based web application for social translation built upon Apertium.
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  • 23
    Java application for training and deploying text processing applications such as part-of-speech taggers, based on a re-implementation of Brill's algorithm in Java.
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  • 24
    Editor for formal grammars. Attempts to be universal – customizable for any grammatical formalism and any syntax. Provides features such as syntax checking and highlighting, transformations (refactoring) and advanced rule editor.
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  • 25
    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.
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