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 Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe…

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 Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about…

Course Start: Semantic Analysis in Language Technology

Spring 2016 Semantic Analysis in Language Technology at Uppsala University (Sweden) http://stp.lingfil.uu.se/~santinim/sais/2016/sais_2016.htm 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 Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe…

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 Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet…