NLP |

Annotation of a Dutch Essay Corpus with Argument Structures and Quality Indicators

Mon, 06 Jun 2022 11:02:28 +0200

Zhang, L., Spoelstra, H., & Kalz, M. (2021). Annotation of a Dutch Essay Corpus with Argument Structures and Quality Indicators. Computational Linguistics in the Netherlands Journal, 11, 257-279.

EMMA

Sat, 10 Apr 2021 23:26:00 +0200

EMMA: European Multiple MOOC Aggregator (funded via Policy Support Program by the European Commission), February 2014 – August 2016, Budget: 4,95 Mio EUR (191.000 EUR acquired for own institution, Role: partner)

EMMA stands for European Multiple MOOC Aggregator. EMMA combines and refines a series of existing components to pilot a web-learning environment where users can access multi-media courseware, translation services, personal learning environments and learning communities. The EMMA platform enables users to access MOOCs in a new, customised way, harvesting content from different courses in their own language. The project seeks to redress the balance between learning communities and individual learning, enabling EMMA users to create their own paths by selecting and integrating different learning objects from other courses along with authoritative resources on the web.

Bildungdigital21

Sun, 07 Mar 2021 08:05:11 +0100

A new direction for the German digital education strategy?

The German government has organized on the 22nd of February 2021 an online-event regarding a future strategy on digital education. The event was a mix of political intention statements and some advertisement of existing initiatives. What I found remarkable was that there is still the vision, that any overarching OER platform would solve any problems with education despite this has failed on the European level already many years ago. The video of the discourse is embedded below.

# We first install the Youtubecaption library
install.packages('youtubecaption')
# Now we import the captions from the video
library(youtubecaption)
library(quanteda)
library(tidyverse)
url <- "https://www.youtube.com/watch?v=WAlP39a5LUU"
caption <- get_caption(url, language ="de")
caption
# Now we need to make a document-term-matrix from the content 
my_corpus <- corpus(caption$text)
summary(my_corpus)
# Extending the stopwordlist with a more extended one from Github
ger_stopwords <- read_lines("https://raw.githubusercontent.com/stopwords-iso/stopwords-de/master/stopwords-de.txt")
custom_stopwords <- setdiff(ger_stopwords, stopwords("german"))
# Constructing the 
meine.dfm <- dfm(my_corpus, remove_numbers = TRUE, remove_punct = TRUE, remove = c(stopwords("german"), custom_stopwords))
meine.dfm.trim <- dfm_trim(meine.dfm, min_docfreq = 1, min_nchar = 3)
# Visualizing the matrix
textplot_wordcloud(meine.dfm.trim, min_size = 1, max_size = 2, max_words = 100)

This code takes the full transcript from youtube, builds a corpus, removes stopwords and visualizes the most frequent words in the resulting tagloud.