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16: Getting Started with Natural Language Processing
So many tweets and news articles and unstructured text surrounds us. How do we make sense of all of these? Natural language processing or NLP can help. NLP refers to algorithms that process, understand and generate aspects of natural language either in text or in spoken voice. In this episode we will cover some of the common techniques in NLP to help get started in this exciting field!
We cover several tasks in a NLP pipeline:
1. Tokenization and punctuation removal
2. Stemming and Lemmatization
3. One hot vectors
4. Word embeddings including Word2Vec and Glove
5. Recurrent Neural Networks and LSTMs
6. tf and tf-idf approaches - when to use word embeddings, when to use tf / tf-idf approaches?
7. Generating text using encoder-decoder or sequence to sequence models
Some resources:
1. Sequence Models - course by Andrew Ng on Coursera - one of the best courses I have seen on this topic! https://www.coursera.org/learn/nlp-sequence-models
2. Awesome collection of resources for NLP for Python, C++, Scala etc. and popular resource: https://github.com/keon/awesome-nlp
3. Overview of Text Similarity Metrics (a blog written by me on Medium): https://towardsdatascience.com/overview-of-text-similarity-metrics-3397c4601f50
4. How to train custom word embeddings on a GPU https://towardsdatascience.com/how-to-train-custom-word-embeddings-using-gpu-on-aws-f62727a1e3f6
Thanks for listening, please support this podcast by following the link in the end.
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By Sanket Gupta
5
55 ratings
So many tweets and news articles and unstructured text surrounds us. How do we make sense of all of these? Natural language processing or NLP can help. NLP refers to algorithms that process, understand and generate aspects of natural language either in text or in spoken voice. In this episode we will cover some of the common techniques in NLP to help get started in this exciting field!
We cover several tasks in a NLP pipeline:
1. Tokenization and punctuation removal
2. Stemming and Lemmatization
3. One hot vectors
4. Word embeddings including Word2Vec and Glove
5. Recurrent Neural Networks and LSTMs
6. tf and tf-idf approaches - when to use word embeddings, when to use tf / tf-idf approaches?
7. Generating text using encoder-decoder or sequence to sequence models
Some resources:
1. Sequence Models - course by Andrew Ng on Coursera - one of the best courses I have seen on this topic! https://www.coursera.org/learn/nlp-sequence-models
2. Awesome collection of resources for NLP for Python, C++, Scala etc. and popular resource: https://github.com/keon/awesome-nlp
3. Overview of Text Similarity Metrics (a blog written by me on Medium): https://towardsdatascience.com/overview-of-text-similarity-metrics-3397c4601f50
4. How to train custom word embeddings on a GPU https://towardsdatascience.com/how-to-train-custom-word-embeddings-using-gpu-on-aws-f62727a1e3f6
Thanks for listening, please support this podcast by following the link in the end.