/NLTK Vs Spacy Vs Stanford CoreNLP

NLTK Vs Spacy Vs Stanford CoreNLP

POS(Part of  Speech) and NER(Named Entity Recognition) are one of the most important tasks in NLP. It’s important to select a library which can perform these tasks with high accuracy and low latency for real world applications. Here is a comparison between the best open source Python libraries in the market.

Feature Availability*

Feature Spacy NLTK Core NLP
Easy installation Y Y Y
Python API Y Y N
Multi Language support N Y Y
Tokenization Y Y Y
Part-of-speech tagging Y Y Y
Sentence segmentation Y Y Y
Dependency parsing Y N Y
Entity Recognition Y Y Y
Integrated word vectors Y N N
Sentiment analysis Y Y Y
Coreference resolution N N Y


Speed: Key Functionalities – Tokenizer, Tagging, Parsing*

Package Tokenizer Tagging Parsing
spaCy 0.2ms 1ms 19ms
CoreNLP 2ms 10ms 49ms
NLTK 4ms 443ms


Accuracy: Entity Extraction*

Package Precition Recall F-Score
spaCy 0.72 0.65 0.69
CoreNLP 0.79 0.73 0.76
NLTK 0.51 0.65 0.58


*Source: www.analyticsvidhya.com

An AI evangelist and a multi-disciplinary engineer. Loves to read business and psychology during leisure time. Connect with him any time on LinkedIn for a quick chat on AI!