Search Results for "tagging"

SCAN (Smart Content Aggregation and Navigation) is a universal semantic content aggregator. It combines search, text analysis, tagging and metadata functions to provide new user experience of desktop navigation and document management.

...Plinko should identify and parse the data completely without the sending system caring what it sent. The latest version supports named fields in the STL files for tagging data parsed in the Prefix Tree and anonymous functions for parsing dynamic message payloads. We now output JSON objects and I'm working on HBase integration. By outputting to JSON it also leaves open the possibility for on the fly in memory correlation between events. Read the included README before starting, it has a quick start guide and info on the constructors.

HanNanum is a Korean Morphological Analyzer and POS Tagger. A plug-in component-based architecture is adapted to the new Java version for flexible use. You can find the work flow for morphological analysis, POS tagging, noun extraction, etc. Contact: kschoi@kaist.ac.kr hjjeong@world.kaist.ac.kr

Based on the Buckwalter Morphological Analyzer (Version 1.0) for doing Arabic stemming and POS tagging. Includes a rewrite of the original Perl script, with better documentation and more flexible options, and a C++ interface (usable as a library or app).

hypKNOWsys aims at developing a Java-based workbench for knowledge discovery and knowledge management. Currently, hypKNOWsys has released two intermediate tools: DIAsDEM Workbench (text mining for semantic tagging) and WUMprep (Web mining pre-processing)

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).

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.

AutoSummary uses Natural Language Processing to generate a contextually-relevant synopsis of plain text. It uses statistical and rule-based methods for part-of-speech tagging, word sense disambiguation, sentence deconstruction and semantic analysis.