The NLP
Canvas!
About
NLP techniques to build
an Automatic Question
Answering System
Have you ever wondered how machines understand Natural Language? How “Google Translate” works? How “Siri”, a robotic voice, responds to your voice commands? Or how a piece of software understands a text document and does automatic summarization or extract relevant sentences? The answer to all these questions lie in this workshop where we explore the astounding domain of Natural Language Processing (NLP). We will unveil the very concepts of NLP with the help of your notion of how you understand the natural human language. With tons of text data being produced every day and with immense amount of efforts carried out to build a voice-controlled world, the domain of Natural Language Processing is gaining an exponential attention with simultaneous advances in Machine Learning and Deep Learning.
Outline
Combination of theory
and practical examples
Total duration of the tutorial will be 3 hours.
- What is Machine Learning?
- What is Natural Language Processing?
- Brief insights on how natural language is studied.
- Why Machine Learning over rule based methods?
- Understanding the NLP Pipeline!
- Demo and code walk through
- Question and Answers (15 mins)
■ Cleaning
➢ Regular Expression
■ Chunking
➢ Paragraph Detection
➢ Sentence Boundary Detection
➢ Sentence to Words
Feature Engineering for ML techniques
■ Grammar
■ Words: Meanings, Synonyms, Antonyms, Part Of Speech (Verb, Adverb, Cardinal) etc.
■ Named Entity Relation, Dependency Parsing, Coreference Resolution etc.
■ Word Normalization: Lemmatization and Stemming
■ Keyword recognition
■ WordNet, Synsets, Stanford Core NLP Parser, spaCy, NLTK
Vector representation and Word Embeddings
■ Why vector or embedding is required?
■ Bag of Words, n-gram Model
■ Skip - gram model
■ Count Vectorizer
■ Term Frequency - Inverse Document Frequency Vectorizer
■ Hashing Vectorizer
■ Automatic Feature selection and vector representation in DL techniques
⟶ Word2Vec
⟶ GloVe
How Scikit learn (sklearn) library comes to the rescue!
Data and ML cookstart!!
■ Corpus
■ Training, Testing and Validation Phase
■ K-fold Cross Validation
Training the Machine Learning Model
■ Evaluation Metrics
How to make your model better and improve the performance?
■ Error Analysis
For two simple examples:
■ If we pass on a document of Roger Federer, the system when asked about his last Wimbledon Championship Title will answer “2017, against Marin Cilic.”
■ If we pass on a Wiki-document of Google, the system when asked about the founder(s) will answer “Larry Page and Sergey Brin”
AQuA can be trained and applied across range of domains and with diverse applications and can save immense amount of time of reading a large-content document.
Team
