Abstract: Natural language processing (NLP) has become an important field with interest from many important sectors that leverage modern deep learning methods for approaching several NLP problems and tasks such as text summarization, question answering, and sentiment classification, to name a few. In this tutorial, we will introduce several of the fundamental NLP techniques and more modern approaches (BERT, GTP-2, etc.) and show how they can be applied via transfer learning to approach many real-world NLP problems. We will focus on how to build an NLP pipeline using several open-source tools such as spaCy and TensorFlow. Then we will learn how to use the NLP model to search over documents based on semantic relationships. We will use open-source technologies such as BERT and Elasticsearch for this segment to build a proof of concept. In essence, the learner will take away the important theoretical pieces needed to build practical NLP pipelines to address a wider variety of problems in the real world.
The target audience for this tutorial are ideally participants with some exposure to the Python programming language and have used natural language processing language tools such as spaCy or NLTK. Knowledge of machine learning tools and concepts is also a benefit as we will be using them in this tutorial. Beginners are also welcome but still require at least some theoretical understanding of NLP and machine learning concepts.
● Follow the Github link for more information;
● Google Colab or Jupyter Notebooks;
Bio: Elvis Saravia is an educator at Elastic and an independent research scientist. He earned his Ph.D. in Computer Science at National Tsing Hua University. His topics of interest include but are not limited to information retrieval, data analysis, transfer learning, sentiment analysis, intent discovery, natural language modeling, conversational AI, and computational health. Elvis is also lead and editor of dair.ai which aims to democratize AI research, education, and technologies.