MITIE: MIT Information Extraction offers state-of-the-art information extraction tools.
There are tools for performing named entity extraction and binary relation detection as well as tools for training custom extractors and relation detectors.
The core MITIE software is written in C++, but bindings for several other software languages including Python, R, Java, C, and MATLAB allow a user to quickly integrate MITIE into his/her own applications.
MITIE is built on top of dlib, a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. MITIE’s primary API is a C API.
Key Features
- Uses several state-of-the-art techniques including the use of distributional word embeddings and Structural Support Vector Machines.
- Several pre-trained models providing varying levels of support for both English, Spanish, and German trained using a variety of linguistic resources.
- Comes with a basic streaming Named Entity Recognition (NER) tool. Its NER implementation is designed for bulk data processing at high speeds.
- Compile MITIE as a shared library.
- Compile MITIE using OpenBLAS.
- Use MITIE from a Python 2.7 program, from R, from a C program, from a C++ program, and from a Java program.
Website: github.com/mit-nlp/MITIE
Support: mitie-trainer – an interactive, browser-based model training tool for MITIE
Developer: Davis E. King and contributors
License: Boost Software License
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Related Software
| C++ Natural Language Processing Tools | |
|---|---|
| text2vec | Framework with API for text analysis and natural language processing |
| Moses | Statistical machine translation system |
| TiMBL | Tilburg Memory-Based Learner |
| MITIE | MIT Information Extraction |
| MeTA | Modern C++ data sciences toolkit |
| Colibri Core | Efficient n-gram & skipgram modelling on text corpora |
| CRF++ | Yet Another CRF toolkit |
| BLLIP Parser | Statistical natural language parser |
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