Search Results for "p-unit"
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Showing 6 open source projects for "p-unit"
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1
Drug Extraction
Drug name extraction
...Using CONLL-Evaluation: processed 32065 tokens with 3656 phrases; found: 3251 phrases; correct: 2786. accuracy: 95.25%; precision: 85.70%; recall: 76.20%; FB1: 80.67 Using GATE Corpus Benchmark: Strict: P: 0.65 R: 0.73 F1: 0.69 Lenient: P: 0.74 R: 0.84 F1: 0.78 The details of how to reproduce evaluation, see README. To use standalone version for tagging download DrugExtractionStandalone.tar.gz from Files. -
2
LexSub
A Lexical Substitution Framework
Lexical substitution framework for supervised all-words lexical substitution using delexicalized features. For a runnable (but GPL-licensed) version of LexSub, see LexSub-GPL (sf.net/p/lexsub/lexsub-gpl) -
3
Bermuda Text-to-Speech
This project includes basic NLP and DSP techniques for Text-to-Speech
See TTS demo at: http://rslp.racai.ro/index.php?page=tts This is an entirely written in JAVA project which includes a set of tools and methods designed to enable Multilingual Text-to-Speech (TTS) synthesis. We currently support English and Romanian but we will soon train more models and make them available for download. If you want to read more about our other NLP and TTS tools check out http://nlptools.racai.ro. -
4
miac-p
Code for syntactic parsing and other NLP apps.
Code for syntactic parsing and other natural language processing applications. -
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5
FullFiller
Data Base Benchmarking tool
...It fills MySQL tables columns; perform customized tests; and outputs the results on CSV format. It uses Xeger, a java package for generating random text from regular expressions (http://code.google.com/p/xeger/). Xeger uses dk.brics.automaton java package developed by Anders Møller (http://cs.au.dk/~amoeller/automaton/index.html). -
6
This project is contains implementations of algorithms to integrate the output of different NLP tools (part of speech taggers, morphologies, parsers, etc.) in order to obtain more accurate, more robust and more fine-grained linguistic analyses. Note that the code is outdated, but left here for documentation purposes. Its functionality may be reimplemented within the NLP2RDF project (http://code.google.com/p/nlp2rdf).
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