Computational Linguistics - Intermediate Course

Explore lemmatization, reducing words to their base forms to unify morphology, using dictionary-based and rule-based methods with examples like runs and running becoming run, and cats becoming cat.

Explore sentiment analysis, also called opinion mining, to detect emotional tone and polarity in text. See how context and domain specificity affect results, with TextBlob demonstrations.

Explore sentiment analysis with spaCy and TextBlob, install and configure the spaCy TextBlob pipeline, analyze polarity and subjectivity across three sentences, and troubleshoot common errors.

Learn how the r prefix defines raw strings in Python by placing R before the opening quote, ensuring literal interpretation for regular expressions and avoiding escape characters.

Extract all digits from the given string using the finditer function, store the resulting match objects in a variable, and print each with a for loop.

Solve the exercise by compiling a pattern with an escaped period to match literal periods in the input text, iterate through matches, and print each start index with an f-string.

Apply regex quantifiers to locate all numbers in a string, print every match object for exercise 5, and anticipate the next video with a detailed solution and explanation.

Learn how to modify strings in Python using split and sub, apply regular expressions with the re module, and practice splitting and replacing patterns to manipulate text.

Extract http:// URLs from a text using a regex, identify which among five sample URLs starts with http://, and print each match with the group method using the re module.

Develop a lemmatization function with generative AI, crafting a system prompt to guide the lemmatizer, then compare results with the original text and note stopword removal.

Explore advanced WordNet functionalities, including lemma names and lemmas, parts of speech, antonyms, and synset access by pos and offset, plus language support through langs.

Practice identifying fruit tokens with WordNet in NLTK by tokenizing an input string and filtering tokens that refer to fruits, yielding a list like bananas, oranges, apricots, avocados, watermelon.

solve an exercise by tokenizing the input string, using a synset fruit concept and WordNet hypernyms to identify fruit tokens, collect unique results, and print them.