Tools, OSCAR3, MetaMap, GENIA tagger, GoPubMed, MedLEE, MIST, SProUT, GATE, Charniak-Johnson Parser, GeniaSS, U-Compare, Tarsqi, cTAKES, HIDE

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The Tarsqi Toolkit

The Tarsqi Toolkit (TTK) provides one-stop shopping for all your temporal needs (or, hopefully, at least some of your needs). It integrates extraction of events and time expressions with creation of temporal links, using a set of mostly independent modules, while ensuring consistency with a constraint propagation component. The glue that keeps all the modules together is the TimeML language.

TTK contains the following components:

GUTime - extraction of time expressions
Evita - event extraction
Slinket - modal parsing
S2T - temporal repercussions of modal relations

U-Compare

U-Compare is an integrated text mining/natural language processing system based on the UIMA Framework.

For any UIMA component, an integrated platform of

GUI for easy drag-and-drop workflow (UIMA CPE/component descriptor) creation
comparison by U-Compare parallel component
evaluation, statistics and visualizations
no installation required, click "Start U-Compare" button below to try U-Compare now!

The world largest repository of ready-to-use type compatible UIMA components

fully compatible with the U-Compare type system
just drag-and-drop to use via the integrated system

GENIA Sentence Splitter

GENIA Sentence Splitter (GeniaSS) [1] is a sentence splitter optimized for biomedical texts. GeniaSS reads a text and splits it into sentences by inserting line breaks.

The classification model is based on supervised leaning method using maximum entropy modeling (using simple MaxEnt library[2]).

Trained on the GENIA corpus [3]. The classifier achieved an F-score of 99.7 on 200 unseen GENIA abstracts.

Charniak-Johnson Max-Ent reranking parser

GATE: General Architecture for Text Engineering

GATE is...

open source software capable of solving almost any text processing problem
a mature and extensive community of developers, users, educators, students and scientists
a defined and repeatable process for creating robust and maintainable text processing workflows
in active use for all sorts of language processing tasks and applications, including: voice of the customer; cancer research; drug research; decision support; recruitment; web mining; information extraction; semantic annotation

GENIA Tagger

The GENIA tagger analyzes English sentences and outputs the base forms, part-of-speech tags, chunk tags, and named entity tags. The tagger is specifically tuned for biomedical text such as MEDLINE abstracts. If you need to extract information from biomedical documents, this tagger might be a useful preprocessing tool. You can try the tagger on a demo page.

MetaMap Portal

MetaMap is a highly configurable program developed by Dr. Alan (Lan) Aronson at the National Library of Medicine (NLM) to map biomedical text to the UMLS Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge-intensive approach based on symbolic, natural-language processing (NLP) and computational-linguistic techniques.

SProUT

SProUT (Shallow Processing with Unification and Typed Feature Structures) is a platform for development of multilingual shallow text processing and information extraction systems.

MIST

The MITRE Identification Scrubber Toolkit (MIST) is a suite of tools for deidentifying free-text documents containing personally identifiable information (PII). MIST helps you replace these PII either with obscuring fillers, such as [NAME], or with artificial, synthesized, but realistic English fillers. The transformed documents you create with this toolkit are more likely to meet the requirements of your organization for protecting privacy in documents you distribute.

MedLee

A Medical Language Extraction and Encoding System

The goal of MedLEE is to extract, structure, and encode clinical information in textual patient reports so that the data can be used by subsequent automated processes. This page describes how to use the most recent demonstration version of MedLEE on the Web. MedLEE was created by Carol Friedman in collaboration with the Department of Biomedical Informatics at Columbia University, the Radiology Department at Columbia University, and the Department of Computer Science at Queens College of CUNY.

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