ReVerb is a system that extracts relational triples from text.
Specifically, ReVerb is a program that automatically identifies and extracts binary relationships from English sentences. ReVerb is designed for Web-scale information extraction, where the target relations cannot be specified in advance and speed is important.
ReVerb takes raw text as input, and outputs (argument1, relation phrase, argument2) triples. It takes as input a POS-tagged and NP-chunked sentence and returns a set of (x,r,y) extraction triples. For example, given the sentence “Bananas are an excellent source of potassium,” ReVerb will extract the triple (bananas, be source of, potassium).
A collection of 15 million ReVerb extractions is available for academic use.
Key Features
- Command Line Interface – takes plain text or HTML as input, and outputs a tab-separated table of output. Each row in the output represents a single extracted (argument1, relation phrase, argument2) triple, plus metadata.
- Java Interface.
- Includes a class for training new confidence functions, given a list of labeled examples, called ReVerbClassifierTrainer.
- Easily parallelizable by processing different sentences concurrently.
- Elimination of incoherent extractions.
Website: reverb.cs.washington.edu
Support: Mailing List, GitHub Code Repository
Developer: University of Washington – Anthony Fader, Michael Schmitz, Robert Bart, Stephen Soderland, Oren Etzioni
License: ReVerb Software License Agreement
Open Information Extraction (IE) is the task of extracting assertions from massive corpora without requiring a pre-specified vocabulary.
ReVerb is written in Java. Learn Java with our recommended free books and free tutorials.
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| GATE | Full-lifecycle solution for a broad range of NLP tasks |
| ReVerb | Automatically identifies and extracts binary relationships from sentences |
| NLP4J | NLP framework for JVM languages |
| CogComp-NLP | State-of-the-art Natural Language Processing (NLP) tools |
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