Master Natural Language Processing using case studies

Apply lexical, syntactic, and semantic processing to convert text into meaningful representations. Learn how word forms, grammar, and word meaning drive applications like spam detection, Q&A, and translation.

Develop skills in regular expressions by analyzing quantifiers—zero or more, zero or one, and plus—and applying a find patterns function to match text with patterns.

Master anchors for start and end pattern checks, wildcards for any character, and whitespace handling in regex; learn grouping, alternation, escaping, and flags for case and multiline matching.

Apply preprocessing to text by lowercasing, tokenizing, removing stop words, and joining tokens to build a bag of words representation, then compare limited edition and stemming options on spam data.

Explore the limitations of bag of words and learn how tf-idf uses inverse document frequency to weigh terms by relevance across documents.

Construct an end-to-end spam detection case study with text mining, preprocessing, and a base classifier that achieves around 99% training and 98% test accuracy using feature presence vectors.

Learn to identify and process spelling mistakes and variations in text by applying phonetic hashing, minimum edit distance, and multiword expression techniques to improve NLP preprocessing.

Explains syntactic processing as the next step after lexical processing. Covers grammar basics, parts of speech, stop words, morphology, dependencies, and applications like question answering and analyzing reviews.

Create a lexicon and rule-based models, test with a 70/30 training-test split, compare unigram and bigram approaches, and use regex-backed backoff to improve tagging accuracy.

Apply the Viterbi algorithm to maximize the joint probability of word-tag sequences under a Markov assumption, using training data to compute initial and transition probabilities for accurate tagging.

Learn to estimate emission and transition probabilities for a hidden Markov model from training data, using simple count-based ratios to tag words and sequences.

Develop a Python notebook model using treebank data to train, compute emission and transition probabilities, and build a word-tag probability matrix with visualization via a heat map.

Explore recurrent neural networks (RNNs) and their role in advancing sequential models beyond traditional hidden Markov models, with insights into neural networks and deep learning.

Explore core parsing techniques in natural language processing, including constituency and dependency parsing, context-free grammars, bottom-up and top-down algorithms, and probabilistic CFG, to analyze complex sentences.

Explore context-free grammar through production rules that define how words form noun phrases and other constituents, using terminals, non-terminals, and parse trees to parse sentences.

Explore practical issues in disambiguating meaning in NLP using a fish-and-net example, comparing bottom-up and top-down approaches, and applying a probabilistic method to resolve ambiguity.

Learn to convert a context-free grammar to cnf by breaking right-hand sides with new nonterminals, using A → BC or A → a, and handling ε for the start symbol.

Explore semantic processing to infer meaning from text, tackle word sense disambiguation and semantic association, and learn techniques like the Lesk algorithm to analyze semantics.

explores semantic processing and the meaning of text, showing how context disambiguates word senses and how words are represented as vectors linked to concepts; dumps act as the handle.

Explore how semantic associations and topic modeling identify the main topic of text by mapping relationships between concepts, from football and hockey to diabetes and universities.

Explore key nlp word relationships by examining hypernyms and hyponyms, antonyms, synonyms, meronyms and holonyms, and polysemy and homonyms through practical examples.

Explore WordNet, a semantic lexical database for English useful for semantic processing, that groups words into cognitive synonyms called Seznec and links them through hypernym and hyponym relations.

Explore unsupervised learning with the LEKS algorithm, implementing word sense disambiguation in Python using WordNet definitions, tokenization, and stopword removal.