Category: lectures

Lecture: Sentiment Analysis

polarity similarity

Topics: sentiment analysis, affective meaning, connotational aspects, sentiment lexicons, naive bayes baseline algorithm, mutual information, pointwise mutual information, computational semantics, likelihood, Scherer’s typology, emotion classification, opinion mining, sentiment mining, subjectivity analysis, manually-built sentiment lexicons, semi-supervised methods, SentiWordnet, General Enquirer, earning…

Lecture: Semantic Role Labeling

Semantic Role Labeling

Topics: Semantic Role Labeling, Thematic Roles, Semantic Roles, PropBank, FrameNet, Selectional Restrictions, Shallow semantics, Shallow semantic representation, Predicate-Argument structure, Computational semantics Semantic Role Labeling from Marina Santini

Lecture: Semantics and Computational Semantics

Semantics and Computational Semantics

Topics: logic and language, formal theories, formal semantics, unification, first-order logic, predicate logic, propositional logic, semantics, computational semantics, meaning representation, connotation, denotation. Semantics and Computational Semantics from Marina Santini

Course Start: Semantic Analysis in Language Technology

Spring 2016 Semantic Analysis in Language Technology at Uppsala University (Sweden) Topics: Semantics and Computational Semantics Semantic Role Labelling/Predicate-Argument Structure Sentiment Analysis Word Sense Disambiguation Vector Semantics Information Extraction (I & II) Question Answering (I & II) Ontologies and…

Lecture 9: Machine Learning in Practice (2)

Bag-of-words Representation

Topics: features representation, unbalanced data, multiclass classification, theoretical modelling, real-world implementations, evaluation, holdout estimation, crossvalidation, leave-one-out, bootstrap Lecture 9: Machine Learning in Practice (2) from Marina Santini

Lecture 8: Machine Learning in Practice (I)

Topics: evaluation, t-¬≠test, cost-sensitive measures, occam’s razor, k-statistic, lift charts, ROC curves, recall-precision curves, loss function, counting the cost, weka Lecture 8: Machine Learning in Practice (1) from Marina Santini

Lecture 5: Interval Estimation (ML4LT)

Topics: inferential statistics, statistical inference, language technology, interval estimation, confidence interval, standard error, confidence level, z critical value, confidence interval for proportion, confidence interval for the mean, multiplier, Lecture 5: Interval Estimation from Marina Santini

Lecture 4: Decision Trees (Part 2) (ML4LT)

Topics: attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio from Marina Santini

Lecture 3b: Decision Trees (Part 1) (ML4LT)

Decision Treee

slideshare presentation: Topics: Greediness, Divide and Conquer, Inductive Bias of the Decision Tree, Loss function, Expected loss, Empirical error, Induction.