ICON 2018ICON 2018 - Tutorial

Natural Language Processing and Biomedical Text

Abstract

An enormous amount of biological data have been generated and collected at an unprecedented speed and scale. For example, the application of electronic health records (EHRs) is documenting large amounts of patient data. Automatically extracting different types of knowledge from authoritative texts, e.g., scientific medical literature, electronic health records etc., within the biomedical domain and representing it in a computer analyzable as well as human readable form is an important but challenging goal. Ability to query and use such extracted knowledge-bases can help scientists, doctors and other users in performing tasks such as question-answering, diagnosis, exploring and validating hypotheses, understanding the state-of-the-art, and identifying opportunities for new research. This tutorial covers a good range of research problems in the biomedical domain which can be addressed using natural language processing. We will also discuss the challenges and the possible solutions to the problems in the biomedical domain.

Duration

This tutorial will span over five parts outlined as follows:

Tutorial Outline

  1. Part-1: Types of Biomedical Text  and Their Source (20 Slides, 30 Minutes)
    1. Scientific Medical Literature
    2. Electronic Health Record
    3. Patient Related Textual Data
    4. Online Discussion Forums (Twitter, Blogs etc.)
  2. Part-2: Types of Knowledge in the Biomedical Text (30 Slides, 20 Minutes)
    1. Domain-specific entities, relations, events
    2. Cause-Effect relations
    3. Sentence classification into Introduction, Methods, Results and Discussion (IMRAD) types
    4. Numerical information
    5. High polarity sentences
    6. Comparisons with related work
  3. Part-3: Text mining Approaches w.r.t. Biomedical Text (30 Slides, 30 Minutes)
    1. Rule-based approaches (Sophisticated Regular Expressions)
    2. Machine Learning Approaches (Supervised, Semi-supervised, Unsupervised)
  4. Part-4: Knowledge Extraction Systems from Literature (50 Slides, 70 Minutes)
    1. Joint Extraction of Entities and Relations from Drug Labels using an Ensemble of Neural Networks
    2. An Unsupervised Approach for Cause-Effect Relation Extraction from Biomedical Text
  5. Part-5: Hands on Session (10 Slides, 20 Minutes)
    1. Using ready-to-use NLP tools and library over biomedical text.
      1. To Identify Biological Entities
      2. To Identify Semantic Relation among Entities
  6. Discussion (10 Minutes)

Technical Equipment Required:

  1. Laptop with Python having Stanford-Core-NLP Package
  2. Internet Connection

Mr. Girish Keshav Palshikar

Principal Scientist, TCS Research (TRDDC) Pune

Mail: gk.palshikar@tcs.com

Mr. Girish Keshav Palshikar

Girish Keshav Palshikar obtained an M.Sc. (Physics) from Indian Institute of Technology, Bombay in 1985 and an M.S. (Computer Science and Engineering) from Indian Institute of Technology, Madras in Jan. 1988. Since 1992, he is working in TCS Research, TRDDC, Pune, India, where he is now a principal scientist and leads the Machine Learning R&D Group. In 2012, he was honoured with the title of TCS Distinguished Scientist. Girish has ~125 publications in international journals and conferences. He is also a visiting lecturer at the Computer Science Department of University of Pune and Government College of Engineering, Pune (GCOEP). His areas of research include machine learning, data mining, text mining, natural language processing and their applications to various domains, including fraud detection and human resource management.

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Dr. Raksha Sharma

Scientist, TCS Research (TRDDC) Pune

Mail: raksha.sharma1@tcs.com
raksha@cse.iitb.ac.in

Dr. Raksha Sharma

Raksha Sharma works as a scientist in TCS Research and Innovation. She has received PhD in NLP from Computer Science Department of IIT Bombay in 2017. In her PhD, she was guided by Prof. Pushpak Bhattacharyya. Her areas of research comprise information extraction, sentiment analysis etc. Her work is published in top-notch conferences like ACL, EMNLP, IJCNLP etc. She has been invited speaker in various academic events like IRISS 2016, IRISS 2017, ACL IKDD CoDS 2016, Google AI Summit, Amazon AI Summit etc.

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Mr. Sachin Pawar

Scientist, TCS Research (TRDDC) Pune

Mail: sachin7.p@tcs.com
sachinpawar@cse.iitb.ac.in

Mr. Sachin Pawar

Sachin Pawar works as a Researcher in TCS Research and Innovation. He has received M.Tech. in Computer Science and Engineering IIT Bombay in 2008. He is currently pursuing his PhD at IIT Bombay under the guidance Prof. Pushpak Bhattacharyya and Girish K. Palshikar (TCS Research). His research areas of interest are Information Extraction, Text Mining etc. His work is published in leading NLP conferences like ACL, EACL, IJCNLP etc.

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