Virtueller NLP Hackathon diesen Mittwoch und Donnerstag, 24. und 25. März 2021
by Matthias Stürmer
Guten Tag
Gerne lade ich euch diesen Mittwoch und Donnerstag, 24. und 25. März
2021 zum virtuellen NLP Hackathon der Uni Bern ein. Auf dieser Website
und unten im Email sind die 4 spannenden Challenges aufgeführt, die bis
jetzt eingereicht wurden:
https://www.cnd.philnat.unibe.ch/ueber_uns/aktivitaeten/nlp_hackathon/
Der Ablauf des Hackathons ist wie folgt:
*Kickoff am Mittwoch, 24. März 2021, 9:00 - 10:00 Uhr*
- Begrüssung und Einführung
- Vorstellung der Challenges
- Team-Building
*Präsentation der Resultate am Donnerstag, 25. März 2021, 15:00 - 16:00 Uhr*
- Präsentation der Ergebnisse
- ab 16h virtuelles Abschlussbier
Meeting auf BigBlueButton: https://bbb.ch-open.ch/b/mat-f4n-qtn
Kommunikation per Slack: https://nlphackathon.slack.com
Aktuell sind rund 25 Personen angemeldet. Wer ebenfalls teilnehmen
möchte, kann sich per Email an dh(a)wbkolleg.unibe.ch anmelden.
Danke auch fürs Weiterleiten dieser Nachricht an weitere interessierte
Personen!
Wir freuen uns auf spannende zwei Tage NLP-Hacking!
Herzliche Grüsse,
Matthias Stürmer
Challenges
Folgende vier Challenges sind aktuell eingereicht:
1. Forschungsstelle Digitale Nachhaltigkeit Uni Bern: Kompetitive
Challenge "Klassifikation von Schweizer Gerichtsurteilen"
<https://www.kaggle.com/c/swiss-german-court-rulings/overview>
The legal language is very special in many regards compared to
regular natural language. It is highly structured, rather
complicated, contains its own special terms and uses certain words
differently than they are used in regular text. Text classification
is simple to define but has a myriad of possible applications and
good systems can provide immense value. Common general applications
of text classification include for example spam filtering, email
priority rating, or topic classification. And in the legal domain
text classification includes legal judgement prediction (predict
outcome of a case based on description of case's facts) or legal
area prediction. So in this challenge, you will predict the chamber
based on the text of a court decision. The chamber is structured in
the form of {federal level}_{court}_{chamber number} (e.g. SG_KG_002
=> St. Gallen, Kantonsgericht, 002).
2. Statistisches Amt Kanton Zürich: Kreative Challenge "STATBOT.CH"
<https://www.cnd.philnat.unibe.ch/ueber_uns/aktivitaeten/nlp_hackathon/sta...> (English
Documentation on GitHub
<http://https/github.com/statistikZH/statbot/tree/main/documentation>)
If you are searching for some form of statistical information, it is
not always easy to find it in the shortest time possible.
Particularly in Switzerland, the data and information are not only
spread vertically over different federal levels. They are also
spread within these federal levels horizontally over different
offices and even there sometimes over different sites/channels with
different formats. Looking for the needle in the haystack looks
comparably easy next to that. Further, even search engines are only
of limited help, as they follow an indexing logic that excludes
information stored in databases or files. The background of a more
difficult search for facts, is also a risk for democratic processes:
The harder it is for the average citizen to find truthful
information, the easier it is to spread fake news. Therefore, the
Statistical Office of the Canton of Zurich, together with other
organizations, would like to develop a Swiss Statistical Bot
(STATBOT), which would provide data and statistical information
directly and quickly across all organizations.
3. Digital Humanities Uni Bern: Kreative Challenge "NER for Historical
Documents" <https://www.kaggle.com/c/ner-turmbucher>
Developments towards NER solutions have shown significant outcome in
the past few years already. Nevertheless, applications for sparse
language data are still a challenge, specially when dealing with
data from pre-modern times. In this challenge, we focus on language
data from the 16th to the 18th century from the Bernese Turmbücher
(legal documents protocolled in the Tower of Bern, Switzerland).
These documents are currently hosted in the State Archives of Bern.
Language models are not provided.
4. Digital Humanities Uni Bern: Visualization of Language Models
Language models (e.g. character embeddings) are essential to succeed
in NLP tasks. Especially when it comes to Part-of-Speech and Named
Entity Recognition, tasks result in more precise models if supported
by adequate language models already. Since the advent of word2vec
and large transformer-based language models (such as BERT or GPT-3)
a variety of specialized and fine-tuned language models is currently
available. Despite the widespread use and the necessity when it
comes to specific model training (e.g. for language entities with
only sparse data), our understanding of the models themselves is
limited at best. In order to strengthen our understanding of
language models and to start the process of reflecting them, this
challenge asks for creative ways of visualizing language models. We
envision 3D-visualizations based on dimension reduction to identify
the positioning of e.g. synonym/homonyms in vector spaces or listing
of semantic fields (neighboring vector values). For context
insensitive approaches (e.g. word2vec or GloVe) we imagine to use
the fixed vectors and represent calculations in grids.
__________________________________
Universität Bern
Institut für Informatik
Forschungsstelle Digitale Nachhaltigkeit
PD Dr. Matthias Stürmer
Leiter der Forschungsstelle Digitale Nachhaltigkeit,
Dozentur Digitale Transformation am INF und
Dozentur Digitale Nachhaltigkeit am IWI
Büro 204 (2. Stock)
Schützenmattstrasse 14
CH-3012 Bern
Telefon +41 31 631 38 09 (Direkt)
Telefon +41 31 631 47 71 (Sekretariat)
Mobile +41 76 368 81 65
matthias.stuermer(a)inf.unibe.ch
www.digitale-nachhaltigkeit.unibe.ch
2 years