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This is a suite of several software agents to provide a complete architecture of lexical base as proposed in Didier Schwab's PhD. thesis. It will be used for automatic translation, information retrieval and other natural language processing tasks.
The program provides Java interface (to C++ Lemmatizer via XML-RPC) in order to perform lemmatizing in Russian, English, and German (lemma is the canonical form of a lexeme in Natural Language Processing). RussianPOSTagger could work as a module of GATE.
Complete technical analysis & trading system, full set of features: retrieve, analyze EOD stocks data; manage multiple portfolios; technical analysis & graphical rendering; neural networks for generation of trading signals; support trader community
OntoExtractor is a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. From a set of XML documents the application generates a taxonomy. OntoExtractor has been developed so far by the Kn
The aim of MIEX (Metadata and Information Extractor from small XML documents) is to create a wrapper for the Stanford Parser, to extract and store metadata (syntactic structures, relationships among words...) from simple XML documents.
JWebPro: A Java tool that can interact with Google search and then process the returned Web documents in a couple of ways. The outputs can serve as inputs for NLP, IR, infor extraction, Web mining, online social network extraction/analysis applications.
Java Expert Rule Based Inference Language. Jerbil is an open source rule processing engine written in Java. Currently Jerbil supports a full set of processing functions with text-based and XML interfaces; a Java interface is planned.
Agent-based Grid simulator built on top of Repast simulation engine. Allows for a quick and easy development and analysis of agent-based coordination mechanisms for the Grid
The Word Vector Tool is a simple but flexible Java library to create word vector representations of text documents. Word vectors can be used for various text processing tasks, as text classification, text clustering or information retrieval.
HDP-IS is the engine of a intellectual services rendering system. The purpose of the project is analysis and implementation of new control models and tools of information interaction during process of rendering of intellectual services.
Open Source Semantic Web Search Engine Software: If two machines anywhere on the web can agree on the same definition of a digital service or digital good, then machine to machine transactions can use this lingua franca to transact on the users behalf.
JVnSegmenter is a Java-based and open-source Vietnamese word segmentation tool. The segmentation model was trained on about 8,000 sentences using Conditional Random Fields (FlexCRFs). This tool would be useful for Vietnamese NLP community.
Evidence-based Guideline and Decision Support System. Provides patient specific point of care reminders in order to aid physicians provide high quality care. Input/output in the form of HL7 CDA Level 2 documents. Knowledge is encoded using Arden Syntax.
This project consists in a simulation software of robot A.I. It is aimed at comparing the efficiency of robot intelligence against movement tasks between fixed checkpoints in a logical world.
Qualiweb aims at providing semantic web metrics for modeling a website visitors needs according to a given taxonomy or document classification. Web metrics provided by Qualiweb give an indication of how successful each of the website topics have been.
JTextPro: A Java-based Text Processing tool that includes sentence boundary detection (using maximum entropy classifier), word tokenization (following Penn conventions), part-of-speech tagging (using CRFTagger), and phrase chunking (using CRFChunker).
CRFChunker: Conditional Random Fields Phrase Chunker (Phrase Chunking Tool) for English. The model was trained on sections 01..24 of WSJ corpus and using section 00 as the development test set (F1-score of 95.77). Chunking speed: 700 sentences/s
CRFTagger: Conditional Random Fields Part-of-Speech (POS) Tagger for English. The model was trained on sections 01..24 of WSJ corpus and using section 00 as the development test set (accuracy of 97.00%). Tagging speed: 500 sentences/s.