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BLLIP Parser

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26 reviews
  • 4 profiles
  • 4 categories
Average star rating
4.5
Serving customers since
2003

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Star Rating

16
9
1
0
0

BLLIP Parser Reviews

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Profile Name
Star Rating
16
9
1
0
0
Chandrashekhar K.
CK
Chandrashekhar K.
Clemson Univ ( MS Electrical Engg.-Intelligent sys). Data Project Specialist at Hertzler systems
12/01/2021
Validated Reviewer
Review source: G2 invite
Incentivized Review

Python NLP for Machine Learning Review

Very useful for Machine Learning and Data Science. It contains a lot of valuable modules for language processing.
Luis M.
LM
Luis M.
06/17/2021
Validated Reviewer
Review source: G2 invite
Incentivized Review

An easier way to work with data

I like the capacity of the tool to work with a large quantity of data
Dejanira B.
DB
Dejanira B.
PhD em Ciências Atmosféricas
06/15/2021
Validated Reviewer
Verified Current User
Review source: Organic

Best and most complete library for data analysis

I'm a meteorologist and I work with a large database. PyNLPI is fundamental for optimizing my work. Working with Python and without a shadow of a doubt it is the best tool in data analysis.

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What is BLLIP Parser?

The BLLIP Parser, also known as the Brown Laboratory for Linguistic Information Processing Parser, is a sophisticated natural language processing tool designed for syntactic parsing of English text. Developed by the Brown University's BLLIP lab, this parser utilizes statistical models to analyze and interpret sentence structures, making it highly effective for various applications in computational linguistics and language technology.Originally based on the well-regarded Charniak Parser, the BLLIP Parser has undergone significant enhancements and updates to increase its accuracy and performance. It features a rich set of tools for training new models from annotated corpora, thereby allowing customization and improvements tailored to specific language tasks or datasets.The BLLIP Parser is widely used in academic and commercial settings for tasks such as information extraction, question answering, and machine translation preprocessing. It's available for download and integration into projects, offering robust parsing capabilities that leverage advanced machine learning techniques.

Details

Year Founded
2003