Open Source Java Linguistics Software - Page 5
Sort By:
Java Linguistics Software
View 201 business solutionsBrowse 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.
-
Curtain LogTrace File Activity MonitoringCurtain LogTrace File Activity Monitoring is an enterprise file activity monitoring solution. It tracks user actions: create, copy, move, delete, rename, print, open, close, save. Includes source/destination paths and disk type. Perfect for monitoring user file activities.
-
Powering the next decade of business messaging | Twilio MessagingXBuild unique experiences across SMS, MMS, Facebook Messenger, and WhatsApp – with our unified messaging APIs.
-
1
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.
-
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
-
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. -
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.
-
World class QA, 100% done-for-youMuukTest is a test automation service that combines our own proprietary, AI-powered software with expert QA services to help you achieve world class test automation at a fraction of the in-house costs.
-
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 -
6
-
7
-
8
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. -
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. -
Automate Proposals with AI in Microsoft Word.Automate your best practices, processes, and standards to guide your proposal writers, sales teams, and subject experts. And don’t worry, it’s so easy to use they will use it. We would love the opportunity to help you quantify the impact your business can expect from investing in Expedience Software. Click here to request a Return on Investment (ROI) calculation. In this 15-minute session, we will ask 20 simple questions to assess and grade your current proposal quality and scalability. Manual proposal processes are likely costing you far more than you realize. These models waste time and kill the productivity of proposal writers, sales team members, senior staff, and subject experts.
-
10
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%. -
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
-
12
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 -
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 -
14
-
15
Texalyzer
Text analyzer
Analyzes text document using TF-IDF and optionally stopword list, and extracts important keywords. -
16
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. -
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. -
18
-
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). -
20
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. -
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.
-
22
-
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.
-
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.
-
25
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.
