This is the ninth article in my series of articles on Python for NLP. In the previous article, we saw how Python’s Pattern library can be used to perform a variety of NLP tasks ranging from tokenization to POS tagging, and text classification to sentiment analysis. Before that we explored the TextBlob library for performing similar natural language processing tasks. In this article, we will explore StanfordCoreNLP library which is another extremely handy library for natural language processing. We will […]

This is the 10th article in my series of articles on Python for NLP. In my previous article, I explained how the StanfordCoreNLP library can be used to perform different NLP tasks. In this article, we will explore the Gensim library, which is another extremely useful NLP library for Python. Gensim was primarily developed for topic modeling. However, it now supports a variety of other NLP tasks such as converting words to vectors (word2vec), document to vectors (doc2vec), finding text […]

This is my 11th article in the series of articles on Python for NLP and 2nd article on the Gensim library in this series. In a previous article, I provided a brief introduction to Python’s Gensim library. I explained how we can create dictionaries that map words to their corresponding numeric Ids. We further discussed how to create a bag of words corpus from dictionaries. In this article, we will study how we can perform topic modeling using the Gensim […]

This is the 12th article in my series of articles on Python for NLP. In the previous article, I briefly explained the different functionalities of the Python’s Gensim library. Until now, in this series, we have covered almost all of the most commonly used NLP libraries such as NLTK, SpaCy, Gensim, StanfordCoreNLP, Pattern, TextBlob, etc. In this article, we are not going to explore any NLP library. Rather, we will develop a very simple rule-based chatbot capable of answering user […]

This is the 13th article in my series of articles on Python for NLP. In the previous article, we saw how to create a simple rule-based chatbot that uses cosine similarity between the TF-IDF vectors of the words in the corpus and the user input, to generate a response. The TF-IDF model was basically used to convert word to numbers. In this article, we will study another very useful model that converts text to numbers i.e. the Bag of Words […]

This is the 14th article in my series of articles on Python for NLP. In my previous article, I explained how to convert sentences into numeric vectors using the bag of words approach. To get a better understanding of the bag of words approach, we implemented the technique in Python. In this article, we will build upon the concept that we learn in the last article and will implement the TF-IDF scheme from scratch in Python. The term TF stands […]

This is the 15th article in my series of articles on Python for NLP. In my previous article, I explained how to implement TF-IDF approach from scratch in Python. Before that we studied, how to implement bag of words approach from scratch in Python. Today, we will study the N-Grams approach and will see how the N-Grams approach can be used to create a simple automatic text filler or suggestion engine. Automatic text filler is a very useful application and […]