Python wrapper of lightening fast Finite State machine and REgular expression manipulation library
Hi, we are a team at Microsoft called Bling (Beyond Language Understanding), we help Bing be smarter. Here we wanted to share with all of you our FInite State machine and REgular expression manipulation library (FIRE). We use Fire for many linguistic operations inside Bing such as Tokenization, Multi-word expression matching, Unknown word-guessing, Stemming / Lemmatization just to mention a few.
Bling Fire Tokenizer
Bling Fire Tokenizer is a tokenizer designed for fast-speed and quality tokenization of Natural Language text. It mostly follows the tokenization logic of NLTK, except hyphenated words are split and a few errors are fixed. Currently released model supports most of the languages except East Asian (Chinese Simplified, Traditional, Japanese, Korean, Thai). You should expect good results if a language uses space as a main token delimitter. The tokenizer high level API designed in a way that it requires 0 configuration, or initialization, or additional files and is friendly for use from languages like Python, Perl, C#, Java, etc. It is fast as uses deterministic finite state machines underneath.
Comparing Bling Fire with other popular NLP libraries, Bling Fire shows 10X faster speed in tokenization task
|System||Avg Run Time (Second Per 10,000 Passages)|
See more at benchmark wiki
To start using Bling Fire Library and Finite State Machine manipulation tools, you can build the project on Windows/Linux with CMake. You need this if you want to create your own tokenization / segmentation, stemming etc. logic or need finite state machines for any other need. Read more here.
If you simply want to use it in Python, you can install the latest release using pip:
pip install blingfire
from blingfire import * text = 'This is the Bling-Fire tokenizer' output = text_to_words(text)
This notebook demostrates how Bling Fire tokenizer helps in Stack Overflow posts classification problem.
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Licensed under the MIT License.
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