Search Results for "java dependency"

CodeLocator is a developer tool designed to help analyze, navigate, and understand large Android codebases — especially useful when working with obfuscated code, many dependencies, or when you don’t have full context (e.g. in large apps or reverse-engineering contexts). It provides capabilities to quickly locate references, method calls, resource usages, and dependency paths across the project, which can dramatically speed up understanding code flow, tracing bugs, or auditing code. By giving...

CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. CoreNLP currently supports 6 languages, Arabic, Chinese, English, French, German, and Spanish.

Maestro is a large-scale workflow orchestration platform originally developed by Netflix to coordinate complex data processing and machine learning workflows across distributed systems. The system acts as a general-purpose workflow orchestrator that manages the execution, scheduling, monitoring, and recovery of large pipelines used for analytics and AI operations. It was designed to support the demanding internal infrastructure of Netflix, where thousands of workflows must process massive...

VnCoreNLP is a Java-based natural language processing toolkit tailored for Vietnamese. It offers a fast and accurate pipeline for essential NLP tasks, facilitating research and application development in Vietnamese language processing. ​

CRGREP searches for matching text in databases, various document formats, archives and other difficult to access resources. A command line tool for name and content text matching in database tables, plain files, MS Office documents, PDF, archives, MP3 audio, image meta-data, scanned documents, maven dependencies and web resources. CRGREP will search resources within resources of any arbitrary combination or depth, so text within a document within a zip archive, and so on. Here you...

The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners. Obtaining the teachingbox: FOR USERS: If you want to download the latest releases, please visit: http://search.maven.org/#search|ga|1|teachingbox FOR DEVELOPERS: 1) If you use Apache Maven, just add the following dependency to your pom.xml: <dependency> <groupId>org.sf.teachingbox</groupId> <artifactId>teachingbox-core</artifactId> <version>1.2.3</version> </dependency> 2) If you want to check out the most recent source-code: git clone https://git.code.sf.net/p/teachingbox/core teachingbox-core Documentation: https://sourceforge.net/p/teachingbox/documentation/HEAD/tree/trunk/manual/

VnDP is a Vietnamese dependency parsing toolkit which integrates a pre-trained parsing model and a pre-trained POS tagging model. The parsing model was trained on our VnDT Vietnamese dependency Treebank which was automatically converted from the Vietnamese constituent Treebank. See more details in VnDP's website at http://vndp.sourceforge.net/

LillyTab is a modular description logic consistency checker (reasoner) with support for a subset of OWL/DL.

MSTParser is a non-projective dependency parser that searches for maximum spanning trees over directed graphs. Models of dependency structure are based on large-margin discriminative training methods. Projective parsing is also supported. mstparser 0.5.1 is now available via Maven Central. If you use Maven as your build tool, then you can add it as a dependency in your pom.xml file: <dependency> <groupId>net.sourceforge.mstparser</groupId> <artifactId>mstparser</artifactId> ...

JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.

Darwin 2: Java Framework for Evolutionary Computation (genetic algorithm, GA). A true framework with out-of-the-box functionality and extensibility of all classes. Interface-based pattern with dependency-injection to configure components.

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.

GraphSpider is a pattern matcher which searches parsed text in phrase-structure tree or dependency graph format for syntactic structures matching a set of patterns in MPL, a regexp-like pattern language. Applications: information extraction, text mining.

Sample application showcasing usage of technologies such as: * OSGi R7 Promises for asynchronous generation of transcriptions and linguistic analyses * OSGi R7 Push Stream and JAX RS Server Sent Events for push notifications of processing status * Apache Camel 2.23.1 and RabbitMQ 3.7 for asynchronous communication between services * JPA 2.1 and Hibernate 5.2.12, along with OSGi R7 JPA and Transaction Control services, for persistence layer * OSGi R7 HTTP and JAX RS Whiteboard for...