LaLoCo lab still in the cloud (Fall 2020 update)
Wed 26 August 2020 by Adrian BrasoveanuIn the last two years, we continued to meet around a series of tutorials on Bayesian methods for data analysis and cognitive modeling, with a focus on Signal Detection Theory (2018-19), and around a series of tutorials on deep learning (2019-20).
We might not meet regularly in 2020-21 given Covid-19 and the CZU Lightning Complex fires. If we end up meeting, possible topics might include tutorials on reinforcement learning and its connections to production-based computational cognitive models for linguistic phenomena.
Feel free to email (abrsvn at ucsc.edu) if you need more information and/or access to the materials.
LaLoCo lab in the cloud (Fall 2018 update)
Since the summer of 2015, the LaLoCo lab online presence has moved to a UCSC google group, with associated resources (meeting reports, literature, code, corpora etc.) stored and updated in the cloud (the UCSC google drive).
We continued to meet on average about 7-9 times per quarter, with a good …
read moreComputing Dynamic Meanings: Building Integrated Competence-Performance Theories for Semantics [ESSLLI 2018 Course]
Language and Computation track advanced course, ESSLLI 2018 ++ Instructors: Jakub Dotlačil, Adrian Brasoveanu ++ August 6-10, 2018, 2:00-3:30 pm
This course introduces a new framework that integrates (i) formal syntactic and semantic theories, (ii) mechanistic processing models, and (iii) Bayesian methods of data analysis and parameter estimation. The main …
read moreComputing Dynamic Meanings: Day 1 [ESSLLI 2018 Course]
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Introduction to the ACT-R cognitive architecture
types of knowledge, modules, buffers, chunks, basic subsymbolic components
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intro to the symbolic system: a simple agreement model; intro to the environment: a top-down parser; code available here: example1.py, example2.py, example3.py
exercise code stubs: exercise1.py, exercise2 …
Computing Dynamic Meanings: Day 2 [ESSLLI 2018 Course]
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Introduction to syntactic and semantic parsing in ACT-R/pyactr
syntactic and semantic aspects of incremental interpretation
parsers: topdown_parser.py, bottomup_parser.py, leftcorner_parser.py
Computing Dynamic Meanings: Day 3 [ESSLLI 2018 Course]
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Introduction to Bayesian statistical modeling for linguists
introduction to Bayesian methods for data analysis / cognitive modeling
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Modeling linguistic performance and Bayesian estimation of ACT-R model parameters
we introduce subsymbolic declarative memory components of ACT-R that are essential for modeling linguistic performance — i.e., actual human behavior in experimental …
Computing Dynamic Meanings: Days 4-5 [ESSLLI 2018 Course]
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Mechanistic processing models for formal semantics (DRT + ACT-R + Bayes)
we introduce mechanistic processing models for formal semantics that integrate dynamic semantics, specifically, Discourse Representation Theory (DRT, Kamp 1981, Kamp & Reyle 1993), and the ACT-R cognitive architecture
we show how to embed these mechanistic processing models into Bayesian models …
Spring-Summer 2015 Meetings
We met regularly throughout the months of June, July, and August working on a joint project investigating the formal semantics of disfluency and its interaction with other semantic phenomena, quantification and anaphora in particular.
read moreSpring 2015: Meeting 2
Hitomi Hirayama presented her work on May 26, 12-1:30 pm, the Cave.
Title: Japanese modified numerals and ignorance inferences
Abstract: This study investigates how (modified) numerals in Japanese interact with the particles wa and ga. First, I will point out some differences between English modified numerals and Japanese ones …
read moreSpring 2015: Meeting 1
Our first lab meeting of the quarter will take place this Tue (April 14), 12-1 pm, in the Cave. We will discuss the basics of the Python ACT-R library / platform. If you want to take a look, an extended tutorial is available here:
read more