{{{credits}}}
| L | T | P | C |
| 3 | 0 | 0 | 3 |
- To learn language models
- To understand the levels of knowledge in language processing
- To develop NLP applications.
{{{unit}}}
| UNIT I | OVERVIEW AND LANGUAGE MODELING | 8 |
Origins and challenges of NLP – Knowledge in language processing – NLP applications; Language Modeling: Language and grammar – Grammar-based language models – Lexical functional grammar – Government and binding; Statistical Language Model: N-gram model – Smoothing techniques.
{{{unit}}}
| UNIT II | WORD LEVEL AND SYNTACTIC ANALYSIS | 10 |
Word Level Analysis: Regular expressions – Survey of morphology – Word and sentence tokenization – Stemmer – Word classes – Part-of-Speech Tagging: HMM POS tagging; Syntactic Analysis: Constituency – Context-free grammar – Dependency Grammar; Parsing: Top-down – Bottom-up – Ambiguity – Early algorithm – CYK – Probabilistic CFG – Probabilistic CYK parsing; Tree banks.
{{{unit}}}
| UNIT III | SEMANTIC ANALYSIS | 9 |
The representation of Meaning: Meaning representation – Computational desiderata for representation; Lexical Semantics: Word senses – Relations – WordNet – Thematic roles – Selectional restrictions; Word Sense Disambiguation: Dictionary-based – Supervised – Minimally-supervised – Unsupervised; Word Similarity: Thesaurus methods – Distributional methods.
{{{unit}}}
| UNIT IV | DISCOURSE PROCESSING, IR AND IE | 9 |
Discourse Processing: Reference resolution – Anaphora resolution algorithms – Co-reference resolution; Information Retrieval: The vector space model – Term weighting – Evaluation of IR; Information Extraction: Named entity recognition – Relation detection and classification.
{{{unit}}}
| UNIT V | MACHINE TRANSLATION AND QUESTION ANSWERING | 9 |
Machine Translation(MT): Problems in machine translation – Classical MT – Statistical MT; Factoid Question Answering: Question processing – Passage retrieval – Answer processing – Evaluation of factoid answers.
\hfill Total Periods: 45
Upon the completion of the course the students should be able to:
- Describe the language models (K3)
- Explain levels of knowledge in language processing (K3)
- Apply computational methods in semantic and discourse processing (K3)
- Apply NLP techniques to MT, IR, IE, QA and Summarization systems (K2)
- Apply evaluation metrics for different NLP applications (K3).
- Daniel Jurafsky and James H Martin, “Speech and Language Processing: An introduction to Natural Language Processing, Computational Linguistics and Speech Recognition”, 2nd Edition, Prentice Hall, 2008.
- Tanveer Siddiqui, U S Tiwary, “Natural Language Processing and Information Retrieval”, Oxford University Press, 2008.
- Christopher D Manning, Hinrich Schutze, “Foundations of Statistical Natural Language Processing”, MIT Press, 1999.
- Nitin Indurkhya, Fred J Damerau, “Handbook of Natural Language Processing”, 2nd Edition, CRC Press, 2010.
- Steven Bird, Ewan Klein, “Natural Language Processing with Python”, O’Reilly Media, 2009.
- Ruslan Mitkov, “The Oxford Handbook of Computational Linguistics”, Oxford University Press, 2009.
- NLTK – Natural Language Tool Kit - http://www.nltk.org/.
** CO PO MAPPING :noexport:
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | PSO3 | |
| CO1 | 3 | 2 | 2 | ||||||||||||
| CO2 | 3 | 2 | 2 | ||||||||||||
| CO3 | 3 | 2 | 2 | ||||||||||||
| CO4 | 3 | 2 | 3 | 3 | |||||||||||
| CO5 | 3 | 2 | 2 | ||||||||||||
| Score | 15 | 10 | 3 | 11 | |||||||||||
| Course Mapping | 3 | 2 | 3 | 3 |