Want to hire NLTK developer? Then you should know!
How and where is NLTK used?
- Text Classification: Identifying spam emails
- Sentiment Analysis: Analyzing customer reviews
- Named Entity Recognition: Extracting names of people or places
- Machine Translation: Translating text between languages
- Summarization: Generating concise summaries of articles
- Part-of-Speech Tagging: Identifying parts of speech in a sentence
- Topic Modeling: Analyzing themes in a collection of documents
- Word Tokenization: Breaking text into individual words
- Syntax Parsing: Analyzing the grammatical structure of sentences
- Spell Checking: Correcting spelling errors in text
Compare Junior, Middle, Senior, and Expert/Team Lead NLTK Developer roles
Seniority Name | Years of experience | Responsibilities and activities | Average salary (USD/year) |
---|---|---|---|
Junior | 0-2 years |
| $50,000 |
Middle | 3-5 years |
| $80,000 |
Senior | 6-8 years |
| $110,000 |
Expert/Team Lead | 9+ years |
| $150,000 |
Quick Facts about NLTK.
- NLTK – Natural Language Toolkit, was created in 2001 by Steven Bird.
- Popular projects using NLTK include sentiment analysis and text classification.
- Entry threshold for NLTK is basic Python knowledge and understanding of NLP concepts.
- The most popular related technology to NLTK is spaCy, known for its speed and efficiency.
- Fun Fact: NLTK’s “book” module provides a wealth of examples and exercises for NLP enthusiasts.
TOP NLTK Related Technologies
- Natural Language Toolkit (NLTK)
- Python 3.8
- NumPy 1.19.2
- SciPy 1.5.2
- Matplotlib 3.3.2
- scikit-learn 0.23.2
What are top NLTK instruments and tools?
- WordNet: Lexical database from Princeton Uni, released in 1985
- CMU Pronouncing Dictionary: Carnegie Mellon’s phonetic dictionary, released in 1993
- Stanford POS Tagger: Stanford NLP Group’s part-of-speech tagger, released in 2003
- TextBlob: Simplified text processing from TextBlob contributors, released in 2014
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Maria Lapko
Global Partnership Manager