Teaching
Wed 26 August 2020 by Adrian BrasoveanuSee CV (pdf) for current information about teaching.
Note for prospective graduate students: It is not possible for me to respond to all emails from prospective students, although I would like to. If you are interested in working with me, please take a look at our department’s information about admissions https://linguistics.ucsc.edu/graduate/admissions.html, apply to our MA or PhD program in Linguistics through the regular channels, and feel free to mention my name in your application statement.
Older courses, with links to possibly useful materials:
Spring 2016:
 Semantics B (Ling 232, UCSC), Graduate
Winter 2016:
 Semantics II (Ling 116, UCSC), Undergraduate
Fall 2015:
 Introduction to Linguistics (Ling 50, UCSC), Undergraduate
Summer 2015 (first session):
 Semantics I (Ling 53, UCSC), Undergraduate
Spring 2015:

Seminar in Semantics: Computing Dynamic Meanings — From Montagovian Compositionality to Incremental Processing (Ling 239, UCSC), Graduate
Handouts, summaries, scripts, and miscellaneous other materials associated with the “Computing Dynamic Meanings” seminar:
Winter 2015:
 Semantics A (Ling 231, UCSC), Graduate
 Quantitative Methods in Linguistics (Ling 147, UCSC), Undergraduate
Spring 2014:
 Semantics B (Ling 232, UCSC), Graduate
Sem B syllabus 
Quantitative Methods in Linguistics (Ling 147, UCSC), Undergraduate
Quantitative Methods syllabusLecture notes and slides for Quantitative Methods:
 quant_methods_lecture1.pdf: R as calculator, Variables, Vectors, Lists, More about string manipulation, The basics of working with (local) files
 quant_methods_lecture2.pdf: Quick recap and some related issues, Patterned vectors, Logical operators, Indexing/slicing with logical operators, Set operators, Counting, Sorting, Printing / Saving to a file, Factors
 quant_methods_lecture3.pdf: Basic graphics, Data frames, Saving a data frame to a file, Attaching and detaching data frames, Subsetting data frames, Ordering data frames, Lists, Character / String Processing, More graphics, The Brown Corpus
 quant_methods_lecture4.pdf: Elementary Control (of) Flow Structures (IF, FOR, Example: obtaining the first 12 Fibonacci numbers, WHILE), Taking advantage of the vectorial nature of R and 3 example problems, General programming tips (Topdown design, Debugging and maintenance, Measuring the time your program takes, i.e., very basic profiling), Goals of quantitative modeling and analysis and types of data, Introducing the idea of a probability distribution: Frequency distributions, Becoming familiar with the normal distribution
 quant_methods_lecture4_related_slides1_intro_prob1.pdf: Sample Spaces and Events (Sample Spaces, Events, Axioms and Rules of Probability), Joint, Conditional and Marginal Probability, Bayes’ Theorem, Independence and Conditional Independence, Random Variables and Probability Distributions, Expectation
 quant_methods_lecture4_related_slides2_intro_prob2.pdf: Probability: Frequency vs Reasonable Expectation, Generalizing Classical Logic, Patterns of Plausible Inference (Modus Ponens, Modus Tollens, Affirming the Consequent, Denying the Antecedent, Affirming the Consequent of Weaker/Plausible Implications, Probability Theory as the Logic of Data Analysis
 quant_methods_lecture5.pdf: Introducing least squares models — Data generation, First attempt: the mean of y, Quantifying the error, From sample to population: basic statistical (inductive) inference (Sample size, Sample variance, Putting the two together: the standard error (SE) of the mean, the Central Limit Theorem (CLT), and 95% confidence intervals (CIs)), Understanding the lm output a little bit better: tdistributions, Applying the Central Limit Theorem (CLT) to Bernoulli distributed data (The Bernoulli distribution, The binomial distribution, The binomial distribution and the Central Limit Theorem)
 quant_methods_lecture6.pdf: Second attempt: two means, i.e., predicting y values based on x2 values, The twomean linear (regression) model, Analysis of Variance (ANOVA) tables, Ttests: the simple version of the Ftest we can use for single categorical parameters, Third attempt: ‘many’ means, i.e., predicting y values based on x1 values, Comparing reg1 with the 1mean model, The intercept and slope coefficients (Alternative slopes, Alternative intercepts, Alternative intercepts and slopes)
 quant_methods_lecture7.pdf: Recap and related issues (scatter plot matrices), Rsquared, Correlation, An alternative way of calculating correlation, Inference for simple linear regression: from sample to population, The sampling error for the slope, The sampling error for the intercept, Putting it all together: plotting predictions for linear regression models
 quant_methods_lecture8.pdf: Recap, Fourth attempt: multiple linear regression, Graphical comparison of reg1, reg2 and reg3, ANOVA and model selection, Adding interactions, Interpreting interactions, More on interactions (Multicollinearity and variable centering, Another example of regression with interaction terms)
 quant_methods_lecture9.pdf: Essentials of linear models (The stochastic component of linear models: probability distributions, The deterministic component of linear models: linear predictors and design matrices), Ttests: more realistic examples (Ttest with equal variances, Ttest with unequal variances), Simple linear regression (Interpretation of confidence intervals), Oneway ANOVA, Randomeffects ANOVA, Twoway ANOVA (Aside: using simulation to assess the bias and precision of an estimator, Analysis of twoway ANOVA data), Linear mixedeffects models (randomintercepts models, randomcoefficients model without correlation between intercepts and slopes, randomcoefficients model with correlation between intercepts and slopes)
 quant_methods_lecture10.pdf: Basic introduction to logistic regression, Generalized linear models (GLMs) (The identity link, The log link, The logit link), More about odds and logodds, i.e., logits, The standard logistic distribution, The logistic regression for the CHD~AGE data, A couple of simple examples of GLMs (4 models and their visualization), Model comparison (Deviance and loglikelihood ratios, Background on likelihood functions and maximum likelihood estimates (MLEs), Evaluating the interaction model, Adding polynomial functions as additional predictors)
 quant_methods_lecture11.pdf: Another logistic regression example: manipulating reasonable doubt, Simulating datasets for logistic regression, A linguistic example: /s/deletion in Panamanian Spanish, Long format data & logistic regression
The homework assignments, which were language/linguisticscentric, are available upon request.
Winter 2014:
 Semantics I (Ling 53, UCSC), Undergraduate
Fall 2013:

Semantics III: Computational Formal Semantics with Haskell (Ling 118, UCSC), Undergraduate
The course materials (syllabus, lectures notes, hw assignments, solutions etc.) are provided below. The ghci commands are numbered in the same way in the pdf and the corresponding hs files for ease of reference.Intro to Haskell lecture notes:
 introtohaskell1.pdf, introtohaskell1.hs: the basics, e.g., arithmetic, boolean operators, functions, “if” statements, lists, tuples etc.
 introtohaskell2.pdf, introtohaskell2.hs: types and typeclasses
 introtohaskell3.pdf, introtohaskell3.hs: the syntax for function definitions, e.g., pattern matching, aspatterns, guards, “where” bindings, “let” bindings, “case” expressions
 introtohaskell4.pdf, introtohaskell4.hs: recursion and thinking recursively
 introtohaskell5.pdf, introtohaskell5.hs: curried functions, higher order functions and the structure of complex computations (be they Haskell programs or natural language meanings)
 introtohaskell6.pdf, introtohaskell6.hs: maps and filters, lambdas, function application, function composition
 introtohaskell7.pdf, introtohaskell7.hs: left and right folds, strict and lazy folds, scans
 introtohaskell8.pdf, introtohaskell8.hs: modules (importing them, importing functions from modules, importing modules without certain functions, qualified imports) and intro to the Data.List module
 introtohaskell9.pdf, introtohaskell9.hs: intro to the Data.Char and Data.Set modules, making our own modules, hierarchical modules
 introtohaskell10.pdf, introtohaskell10.hs: intro to algebraic data types, record syntax, type constructors (a.k.a. parametrized types)
 introtohaskell11.pdf, introtohaskell11.hs: deriving instances of typeclasses, type synonyms, recursive data types
Intro to computational formal semantics lecture notes:
 comp_formal_sem1.pdf, comp_formal_sem1.hs, PropLsyn.hs, PropLsem.hs: syntax of propositional logic, semantics of propositional logic, tautologies, satisfiability and contradictions, checking for entailment between two formulas, Context Set update
 comp_formal_sem2.pdf, comp_formal_sem2.hs, PredLsyn.hs, Model.hs, PredLsem.hs: syntax of firstorder predicate logic (the definition of terms, atomic formulas, more on variables, identity, propositional operators, complex terms, quantifiers), semantics of firstorder predicate logic
 comp_formal_sem3.pdf, comp_formal_sem3.hs, EF1syn.hs, EF1sem.hs, PredLsyn.hs, Model.hs, PredLsem.hs: syntax of our English fragment, semantics part 1: EnglishtoFOL translation, semantics part 2: evaluation in a model (model checking)
 comp_formal_sem4.pdf, comp_formal_sem4.hs, EF1syn.hs, EF2sem.hs, Model.hs: syntax of our English fragment, (direct) semantics of our English fragment
 comp_formal_sem5.pdf, comp_formal_sem5.hs, BasicDef.hs, Palindromes.hs, ParserCombinatorsMini.hs: basic definitions for parsing, recognition, generation and parsing for a simple palindrome grammar, parser combinators, simple application of parser combinators: the palindrome grammar, another example: parsing based on a simple Eng. grammar
 comp_formal_sem6.pdf, comp_formal_sem6.hs, BasicDef.hs, Lexicon.hs, ParserCombinators.hs, ParserNoMvt.hs, Tree2Tex.hs: a more realistic lexicon (features, syntactic categories, the lexicon, combining syntactic categories, string preprocessing, the lexer), parsing a more realistic English fragment without mvt (parser for leaf nodes, parser for sentences, NP, DET, CN and PP parsers, VP parser, bringing it all together)
Hw assignments and example solutions:
Winter 2013:
 Semantics II (Ling 116, UCSC), Undergraduate
 Research Seminar (Ling 290, UCSC), Graduate
Fall 2012:
 Semantics I (Ling 53, UCSC), Undergraduate
 Seminar: Introduction to Bayesian Data Analysis & Cognitive Modeling (Ling 239, UCSC), Graduate; see the syllabus for a description of the goals of the course and a tentative schedule
Summer 2012:
 Semantics I (Ling 53, UCSC), Undergraduate
Spring 2012:
 Structure of Romance Languages (Ling 181, UCSC), Undergraduate
Winter 2012:
 Semantics B (Ling 232, UCSC), Graduate

Seminar in Semantics: Statistical & Cognitive Modeling for Formal Semantics (Ling 239, UCSC), Graduate
SyllabusDescription of the broader research program:
Capturing the particular ways in which natural language interpretation proceeds is usually taken to involve rich abstract representations and fairly complex operations over such representations. Under this view, two general goals of formal semantics are to (i) identify patterns of interpretation that seem to involve such abstract (nonovert / latent) representations and operations and (ii) design logical systems in which the ‘right’ range of representations and operators can be defined and in which these representations and operators interact in the ‘right’ way.
At the same time, providing solid empirical foundations for increasingly sophisticated formal semantic theories requires increasingly sophisticated methods of empirical investigation and statistical analysis of the resulting data.
In addition, semantic theories should be complemented and further constrained by cognitive theories of (i) how such structured, abstract and compositionally assembled representations and operations can be learned / induced from ‘raw’ observed data and (ii) the kinds of mechanisms that underlie the processing of such representations and operations in actual natural language usage.
The seminar will focus on establishing and solidifying multiple connections between detailed, formally sophisticated semantic theories (of quantifier scope, interpretation of indefinites etc.) and modern Bayesian methods of data analysis, as well as cognitive models (based on Bayesian ideas) of learning abstract, highly structured representations of the kind deployed in formal semantics.Schedule (subject to change):
 Intro to Statistical & Cognitive Modeling (Week 1)
 Intro to Bayesian Data Analysis (Weeks 14)
 Quantifier Scope, Indefinites and Sentenceinternal Readings (Weeks 48)

Bayesian Cognitive Modeling for Formal Semantics: Learning the Meaning of Number Words and Quantifiers (Weeks 810) * Course materials:

readings available on the eCommons page for the course
 various R scripts, handouts etc. will be posted on the LaLoCo page
Summer 2011:
 Semantics I (Ling 53, UCSC), Undergraduate
Winter 2011:
 Semantics I (Ling 53, UCSC), Undergraduate
 Semantics B (Ling 232, UCSC), Graduate
Spring 2010:

Semantics C: Partiality in Natural Language Semantics (Ling 233, UCSC), Graduate
Time: TuTh 10:0011:45 ++ Location: The CaveDescription: Semantics C will be dedicated to partiality in natural language semantics. We will read the following two books:

R. Muskens (1995), Meaning and Partiality.
In this book, Montague Semantics is partialized by replacing the logic which underlies that system by a partial variant. The result is a theory not unlike Barwise and Perry’s original formulation of Situation Semantics or Kratzer’s present version of that theory: possible worlds become partial possible worlds or situations, ordered by a partof relation. As soon as this natural structure of situations is available within Montague Semantics, the framework supports many analyses of semantic phenomena that were originally carried out within the competing Situation Semantics approach and we thus obtain a synthesis between two semantic frameworks that are usually thought to be incompatible. But there is an important difference between the Barwise and Perry theory and mine: while these authors are revolutionary and seek to replace Montague Grammar by their new theory, my approach is evolutionary. I do not want to abandon Montague Grammar, I want to reform it. I think we simply have not exploited Montague’s paradigm to the full as yet.

E. Krahmer (1998), Presupposition and Anaphora.
Chapter 2 centers around the analyses of anaphoric reference in discourse. It discusses a number of well known solutions (in particular File Change Semantics, Discourse Representation Theory, Pratt’s Quantificational Dynamic Logic, Dynamic Predicate Logic and Montagovian discourse grammars). Chapter 3 discusses negation and disjunction in discourse. Chapter 4 concentrates on the usage of partial logics in the analysis of presupposition. It investigates the relevance of partiality in the current dynamic treatments of presupposition. Chapter 5 focuses on the relationship between presuppositions and classical Montague Grammar. Combining results from chapter 4 with Muskens’ partialization of Montague Grammar results in a system which properly encompasses the Montagovian systems of Hausser 1976, Cooper 1983 and, of course, Karttunen and Peters 1979. The resulting Presuppositional Montague Grammar is both technically clean and empirically satisfactory. Various ways of bringing the system up to the present syntactic and semantic standards are discussed. Chapter 6 studies presuppositions from the dynamic perspective. Chapter 7 is concerned with the presuppositions triggered by definite descriptions.
Course materials:
 Handout 1: Quantification in FOL [Adrian]
 Handout 2: Two type logics (Muskens 1995, Ch. 2) [Adrian]
 Handout 3: PTQ revisited (Muskens 1995, Ch. 4) [Adrian]
 Handout 4: Going partial I (Muskens 1995, Ch. 5) [Robert]
 Handout 5: Going partial II (Muskens 1995, Ch. 6) [Heather]
 Handout 6: Situations & Propositional Attitudes (Muskens 1995, Ch. 7 & Ch. 8) [Kevin]
 Handout 7: Names (Muskens 1995, Ch. 9) [Anie]
 Handout 8: Intro to DRT (Kamp & Reyle 1993, Ch. 1 + part of Ch. 2) [Lauren]
 Handout 9: Intro to DPL+GQ [Adrian]
 Handout 10: Intro to CDRT+GQ [Adrian]
 Handout 11: Invited Lecture, Klaus von Heusinger, Two specific indefinite articles in German
 Handout 12: Negation and Disjunction in DRT (Krahmer 1998, ch. 3 / Krahmer & Muskens 1996) [Boris]
 Handout 13: Karttunen & Peters (1979) followed by Krahmer (1998), Ch. 4 [Nick and Nico]
 Handout 14: Presupposition and Montague Grammar (Krahmer 1998, Ch. 5) [Bern]
 Handout 15: van der Sandt (1992) followed by Krahmer (1998), Ch. 6 [Mark and Oliver]
 Handout 16: Krahmer (1998), Ch. 7 [Nate]


Pragmatics (Ling 117, UCSC), Undergraduate. We will read and discuss in detail chapter 1 and parts of chapters 2 and 4 of Kamp & Reyle (1993) From Discourse to Logic.
 Senior Research Seminar (Ling 190, UCSC), Undergraduate
Winter 2010:
 Semantics I (Ling 53, UCSC), Undergraduate
Spring 2009:
 Semantics II (Ling 116, UCSC), Undergraduate
 Senior Research Seminar (Ling 190, UCSC), Undergraduate
Winter 2009:
 Semantics B (Ling 232, UCSC), Graduate
Time: MW 23:45 ++ Location: The Cave
Office hours: TBA & by email appointment
Description: The goal of the course is to give the participants the technical skills to understand the Montagovian solution to the problem of compositionality  that is, to understand how the meaning of a natural language expression is a function of the meanings of its subexpressions and the way they are syntactically put together. To put it differently, we will learn the basics of rigorously designing a syntaxsemantics interface in the Montagovian tradition. Our specific goal is to be able to read Montague’s PTQ and Hendriks 1993 Ch.1 by the end of the quarter. Our textbook is Dowty et al 1981 Introduction to Montague Semantics. The readings for the course are available on the WebCT page for the course, which you can access by logging into WebCT and selecting this link: LING232 Semantics B, #37056.
Fall 2008:

Seminar in Semantics: Decomposing Quantification (Ling 239, UCSC), Graduate
Time: 1:004:30 ++ Location: The Cave
Office hours: TBA & by email appointmentDescription: The seminar will examine phenomena like correlatives across domains (individuals, times & eventualities, possible worlds and degrees) and the interpretation of same / different in quantificational contexts that support the idea that natural language quantification is a composite notion, to be decomposed / analyzed in terms of discourse reference to dependencies that is multiply constrained by the various components that make up a quantifier. We will examine a variety of languages and a variety of static and dynamic approaches to these phenomena.
For a more detailed description of the project of decomposing quantification, see Decomposing Quantification.Course materials:
 Handout 1 & Syllabus: Cross & IntraSentential Evidence for Decomposing Quantification
 Handout 2: Quantification in FirstOrder Logic
 Handout 3: Intro to DPL slides 6up
 Handout 4: DPL & Dynamic Generalized Quantification
 Handout 5: Compositional DRT & Dynamic Generalized Quantification
 Handout 6: Carlson 1987 [Matt]
 Handout 7: Barker 2007 [Ryan]
 [Handout 8: Brasoveanu 2008]
 Handout 9: Moens & Steedman 1988 [Judith]
 Handout 10: Webber 1988 [Robert]
 Handout 11: Roberts 1989 [Scott]
Spring 2008:
 Semantics (Linguist 130A Introduction to Linguistic Meaning & Linguist 130C Logic Laboratory, Stanford), Undergraduate
Time: Ling 130A  MonWedFri 10:0010:50 AM & Ling 130C  TBA ++ Location: CummsArt 4
Office hours: TBA & by email appointment
Textbook: Introduction to Natural Language Semantics (CSLI Lecture Notes), HenriĆ«tte de Swart
Fall 2007:
 Seminar in Semantics: Indefinites (Ling 237, Stanford), Graduate
Time: TBA ++ Location: TBA
Office hours: TBA & by email appointment
Description: Static and dynamic approaches to the semantics of indefinites (and definites). Their referential vs. quantificational status, their scopal properties (exceptional wide scope), their interaction with modal anaphora & quantification. Indefinites crosslinguistically, types of indefinites (argumental vs. predicative, bare nouns, NPI’s, free choice, whindefinites, specific indefinites etc.) and their semantic / pragmatic properties. Indefinitelike items in the modal, temporal / aspectual & degree domains.
Spring 2007:
 Pragmatics (Ling 117, UCSC), Undergraduate
Time: TuTh 45:45 ++ Location: Engineering Two, 194
Office hours: TBA & by email appointment
Textbook: Pragmatics and Natural Language Understanding (Tutorial Essays in Cognitive Science Series), Georgia Green  Math Foundations (Ling 265, UCSC), Graduate ++ brief course description
Time: TuTh 1011:45 ++ Location: TBA
Office hours: TBA & by email appointment
Winter 2007:
 Introduction to Linguistics (Ling 20, UCSC), Undergraduate
Time: MWF 23:10 ++ Location: Baskin Engineering 152
Office hours: TBA & by email appointment
Fall 2006:
 Introduction to Dynamic Semantics, Graduate, cotaught with Sam Cumming ++ Invited minicourse, Dynamic Semantics Workshop, Institutt for filosofi, ideog kunsthistorie og klassiske (IFIKK), University of Oslo
Here are the syllabus, some of the slides, and the corresponding handout.  Introduction to the Study of Language (Linguistics 101, Rutgers University), Undergraduate
Here are the handouts for the presupposition and conversational implicature (part 1 & part 2) lectures.