Dr. Tafseer Ahmed has been working on Computational Linguistics for the last 20 years. He has worked on theoretical study of grammar as well as on computer applications involving Pakistani languages specially Urdu. He has worked on computational grammar, part of speech tagging, named entity recognition, text mining, author identification, word embedding, smart keyboard and other NLP applications for Urdu and other Pakistani languages. Dr. Tafseer Ahmed got his PhD from Universitaet Konstanz, Germany in 2009. He has teaching and research experience in various institutes including University of Karachi, FAST NUCES Lahore, University of Konstanz, DHA Suffa University, and Mohammad Ali Jinnah University. His post doctoral work was on Computational Grammar of Urdu at University of Konstanz. He has worked on Urdu Propbank for University of Colorado, Boulder. He was co-PI of the DAAD, Germany funded project “Urdu Text to Speech: Understanding Intonation”. Dr. Tafseer Ahmed was publication chair of 4th Conference of Language and Technology 2012, program chair of 5 th Conference of Language and Technology 2014 and convener of International Conference on Data Science 2019.
|Course Title||Course Contents||Duration||Start Date|
|Text Mining-1||Feature Vector Text Classification
Sentiment & Emotion Analysis Lexicon based approaches Collocations
|Computational Linguistics - 1||Morphological Features Part of Speech Tagging Named Entity Recognition Using Parsers||5-week||25-Sep-2020|
|Word Semantics||Word Senses & Lexicon Word Sense Disambiguation Distributional Semantics Word Embedding
|Text Mining - 2||Language Models Spell Checking Information Retrieval Summarization||5-week||18-Dec-2020|
|Deep Learning||Deep Neural Network Recurrent Neural Network Encoder-Decoder Model Attention & Transformer||5-week||29-Jan-2021|
|Computational Linguistics - 2||Morphology
Syntax (Universal Dependency) Semantic Roles
|Applications||Information Extraction Chatbot||5-week||23-Apr-2021|
- Every new course will start after 6 weeks after the start of the previous course. The week will be of teaching, and one week for more practice or rest.
- Most of the lectures will have three parts: theory, programming, and involving your language.
- Weekly lecture length would be of 70-100 minutes.
- How will assessment be done?
- How will the students interact with the instructor?
- What is the scalability of this interaction and assessment approach, in case of hundreds or thousands of students?