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Katrin Erk
  • Home
  • Courses
    • Analyzing linguistic data, and programming for linguists
    • Working with Corpora
    • LIN350 Computational semantics
    • LIN 389C: Research in Computational Linguistics
    • Introduction to computational linguistics
    • LIN313 Language and computers
      • Schedule: LIN313 Language and computers
        • Language and Computers n-gram demo
        • Language technology and society: ethics of artificial intelligence
      • Syllabus: LIN313 Language and computers
    • Seminar on word meaning in context, Spring 2021
      • Schedule: seminar on word meaning in context
      • Syllabus: seminar on word meaning in context
  • Publications
  • About
  • Teaching
    • Resources
    • Students and PostDocs
  • Research
    • Projects
      • DEEPsem: Deep Natural Language Understanding with Probabilistic Logic and Distributional Similarity
        • DEEPsem people
        • DEEPsem publications
        • DEEPsem: Links
      • ISOGRAM: Inference with structured objects representing graded meaning
        • ISOGRAM data
        • ISOGRAM links
        • ISOGRAM people
        • Publications related to the ISOGRAM project
      • Narrative based hypothesis generation using event coherence and probabilistic inference
    • Software and data
      • Picking Apart Story Salads
      • Query-focused scenario construction
Katrin Erk

Resources

Python worksheets

General introduction to Python

  • Worksheet: First steps in Python

  • Worksheet: Building your own Python functions

  • Worksheet: Python conditions, lists, and loops

  • Python list comprehensions

  • Python dictionaries

  • Worksheet: Reading and writing files in Python

  • Worksheet: Sorting in Python

  • Python worksheet: regular expressions

  • Python worksheet: accessing and processing text

Python and computational semantics

  • A very simple distributional model

  • Demo: The building blocks of a distributional model

  • Python demo: gensim

  • Python demo: predicting word similarity

  • Python demo: using a pre-trained space

  • Demo: computational semantics in NLTK

Other Python demos

  • Hidden Markov Models for POS-tagging in Python

  • Demo: the forward-backward algorithm

  • Language models in Python

R worksheets

  • Getting started with R

  • Data exploration

  • R worksheet: plotting

  • R worksheets: merge and aggregate

  • R worksheet: descriptive statistics

  • R worksheet: contingency tables

  • R code: reading, preprocessing and counting text.

  • R code: the t-test

  • R worksheet: influential datapoints

  • R code: nonparametric tests

  • R code: Correlation and linear regression

  • R code: linear regression

  • R code: more linear regression

  • R code: logistic regression

  • R code: model comparison

  • R code: ANOVA

  • R code: classification and cross-validation

  • R code: classification with Naive Bayes

  • R code: classification examples

  • R code: clustering

  • Topic modeling: a demo

Variational inference

Variational LDA tutorial

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