Unit 1
Computational linguistics- Introduction, syntax, semantics, morphology, collocation and other NLP problems.
| Course Name | Text Analytics |
| Course Code | 23AID472 |
| Program | B.Tech in Artificial Intelligence and Data Science |
| Credits | 3 |
| Campus | Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati |
Computational linguistics- Introduction, syntax, semantics, morphology, collocation and other NLP problems.
Word representation: One-hot encoding, Bag-of-Words (BoW) Dictionary: Term Frequency – Inverse Document Frequency (TF-IDF), Language Model-n-gram – Neural Network-based word embedding algorithms
Sequences and sequential data: Machine learning and deep learning for NLP, Sequence to sequence modelling – BERT, GPT, Graph NLP, Hidden Markov Model, Conditional Random Field, Topic modelling
Applications of NLP: Part-of-Speech tagging, Named Entity recognition, Dependency parsing, – Sentiment Analysis, Machine translation, Question answering, Text summarization, Evaluation metrics for NLP models and Visualization
Course Objectives
Course Outcomes
After completing this course, students will be able to
|
CO1 |
Apply modern tools for solving problems in computational linguistics |
|
CO2 |
Implement word representation models to solve NLP problems |
|
CO3 |
Develop machine learning/deep learning models for solving NLP applications |
|
CO4 |
Evaluate the performance of NLP models |
CO-PO Mapping
|
PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
PSO3 |
|
CO |
|||||||||||||||
|
CO1 |
1 |
2 |
1 |
2 |
3 |
– |
– |
1 |
– |
– |
– |
2 |
1 |
– |
1 |
|
CO2 |
2 |
2 |
2 |
2 |
3 |
1 |
– |
1 |
– |
– |
– |
2 |
3 |
2 |
1 |
|
CO3 |
3 |
2 |
2 |
2 |
3 |
1 |
– |
1 |
– |
– |
– |
2 |
3 |
2 |
1 |
|
CO4 |
– |
– |
– |
1 |
2 |
1 |
– |
1 |
– |
– |
– |
1 |
– |
2 |
1 |
Evaluation Pattern
|
Assessment |
Internal/External |
Weightage (%) |
|
Assignments (minimum 2) |
Internal |
30 |
|
Quizzes (minimum 2) |
Internal |
20 |
|
Mid-Term Examination |
Internal |
20 |
|
Term Project/ End Semester Examination |
External |
30 |
Text Books / References
Daniel Jurafsky, James H Martin, Speech & language processing, preparation [cited 2020 June 1] Available from: https://web. stanford. edu/~ jurafsky/slp3 (2018).
Christopher Manning and Hinrich Schütze, Foundations of Statistical Natural Language Processing, MIT press, 1999.
Steven Bird, Ewan Klein and Edward Loper, Natural Language Processing with Python, O’Reilly Media, Inc., 2009.
Jason Browlee, Deep Learning for Natural Language Processing: Develop Deep Learning Models for your Natural Language Problems (Ebook), Machine Learning Mastery, 2017
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