Natural Language Processing with Python (3-part series) – Part 2 of 3 – 2022-05-25
By the end of this series, you should be able to:
- Use popular NLP frameworks in Python, including Gensim and spaCy
- Explain key concepts and terminology in NLP, including dependency parsing, named entity recognition, and word embedding
- Process texts to glean information about sentiment, subject, and style
- Classify texts on the basis of their features
- Produce models of word meanings from a corpus
- Perform a few core NLP tasks including keyword analysis, relation extraction, document similarity analysis, and text summarization.
Software needed: Python; Google Colab (instructors will provide notebooks and data).The copyright on this video is owned by the Regents of the University of California and is licensed for reuse under the Creative Commons Attribution 4.0 International (CC BY 4.0) License.