In (KI3V21001), I explored computational models for natural language processing, focusing on syntax, semantics, and reasoning, assessed via four assignments and a final exam. Below is a breakdown of the topics covered:
Dependency Parsing: Studied dependency grammars and Universal Dependencies.
CCG Parsing: Explored Combinatory Categorial Grammar for syntactic analysis.
Compositional Semantics: Learned to derive meaning from syntactic structures.
Natural Logic & Tableau: Applied tableau methods for natural language inference.
Word Senses & WordNet: Investigated lexical semantics using WordNet.
Natural Language Inference (NLI): Analyzed entailment and related tasks.
Machine Translation: Covered statistical and neural approaches.
Language Diversity: Explored typological variations across languages.
Pretrained Language Models: Introduced to LMs like BERT and their embeddings.
Assignment 1: Parsing: Implemented dependency and constituency parsing with spaCy and CoreNLP, analyzing projectivity and PP-attachment.
Assignment 2: Meaning: Developed WSD systems (Most Frequent Sense, Simple Lesk, Vector-based Lesk) and lexical relation predictors using WordNet and Prolog-based reasoning.
Assignment 3: Translation: Analyzed variation across languages and implemented Byte Pair Encoding for subword tokenization.
Assignment 4: Regression: Trained linear and logistic regression models on GloVe embeddings for concreteness and hypernymy prediction, plus fine-tuned DistilBERT for entailment.