name: Leon Derczynski
status: Assistant Professor of Computer Science
contact:
email: ld@itu.dk (public, subject to FOI requests)
twitter: @leonderczynski
telephone: +45 5157 4948
post: IT University of Copenhagen, Rued Langgaards Vej 7, 2300 Copenhagen, Denmark
ErhvervsForsker: 2019-2022, InnovationsFonden PhD 1,07M DKK.
Deep Learning Generative Models for Content Structuring.
Role: PI for ITU.
NLPL: 2017-2020, NordForsk.
www.nlpl.eu.
Nordic Language Processing Laboratory. A cross-Nordic collaboration of high-performance computing resources and natural language processing resources, between universities and e-infrastructure organisations.
Role: PI for ITU.
COMRADES: 2016-2018, EC H2020 IA €2.0M.
www.comrades-project.eu.
Collective platform for community resilience & social innovation during crises.
Role: Co-I for U.Sheffield.
PHEME: 2014-2017, EC FP7 CP €4.3M.
www.pheme.eu.
Computing Veracity – the Fourth Challenge of Big Data. Pheme builds technology for finding how true claims made online are. This timely project was rated "excellent" at final evaluation.
Role: co-author, scientific co-ordinator.
uComp: 2013-2016, EC CHIST-ERA €1.25M.
www.ucomp.eu.
Embedded Human Computation for Knowledge Extraction and Evaluation. uComp built extensive resources for crowd-sourcing and social media processing, including an easy corpus construction tool integrated with GATE.
Role: named researcher.
TrendMiner: 2011-2014, EC FP7 CP €3.7M. Trendminer on CORDIS. Large-scale, Cross-lingual Trend Mining and Summarisation of Real-time Media Streams.
personal: github.com/leondz
ITU NLP: github.com/ITUnlp
GATE: github.com/GateNLP
Sheffield NLP: github.com/sheffieldnlp
Leon Derczynski
also: Leon Strømberg-Derczynski
View CV
Google Scholar: user=d8iwqa8AAAAJ
ORCID: 0000-0002-8656-3431
DBLP: Derczynski:Leon
2139 citations
20 h-index
35 i10-index
2019 07 08 at date
2018.08.2x: Program co-chair at COLING 2018, Santa Fe
2018.11.01: Dimensions of Variation in User-generated Text at the Workshop on Noisy User-generated Text (W-NUT), Brussels
2018.11.08: Fake News and Troll Detection at SLTC, Stockholm
2018.11.15: Endurance Care at Connecting for Connected Health, Copenhagen
2019.Q1: Guest lecturing ML & NLP at Innopolis University, Kazan, Russian Federation
2019.05.06: Opening keynote at Nordic Disinformation conference
2019.05.23: Automatic Detection of Fake News at PET, the Danish Security and Intelligence Service
2018
COLING: . 2018. Proceedings of the 27th International Conference on Computational Linguistics
ISCRAM: . 2018. Helping Crisis Responders Find the Informative Needle in the Tweet Haystack
2017
book: . 2017. Automatically ordering events and times in text
CSL: . 2017. Generalisation in Named Entity Recognition: A Quantitative Analysis
2016
COLING: . 2016. Broad Twitter Corpus: A Diverse Named Entity Recognition Resource
COLING: . 2016. Representation and Learning of Temporal Relations
AAAI: . 2016. Generalised Brown Clustering and Roll-up Feature Generation
2015
IPM: . 2015. Analysis of Named Entity Recognition and Linking for Tweets
SemEval: . 2015. SemEval-2015 Task 6: Clinical TempEval
WEBIST: . 2015. Enhanced Information Access to Social Streams through Word Clouds with Entity Grouping - best paper
2014
EACL: . 2014. Passive-Aggressive Sequence Labeling with Discriminative Post-Editing for Recognising Person Entities in Tweets
EACL: . 2014. DKIE: Open Source Information Extraction for Danish
EACL: . 2014. The GATE Crowdsourcing Plugin: Crowdsourcing Annotated Corpora Made Easy
2013
ICTIR: . 2013. Information Retrieval for Temporal Bounding
ACL: . 2013. Temporal Signals Help Label Temporal Relations
SemEval: . 2013. SemEval-2013 Task 1: TempEval-3: Evaluating Events, Time Expressions, and Temporal Relations.
HT: . 2013. Microblog-Genre Noise and Impact on Semantic Annotation Accuracy
EDBT: . 2013. Towards Context-Aware Search and Analysis on Social Media Data
2012
LREC: . 2012. TIMEN: An Open Temporal Expression Normalization Resource
Relative position of Starman (live data)
EARTH:km :mi VEL:km/s :km/h :mph (origin)
I'm always open to supervising motivated and capable students, for thesis or other project work.
Open projects are described on a dedicated page:
View open research projects.
_ █
2019.05 I wrote a series of data-driven articles on the Danish national election for the press (Danish): Mandag Morgen
2018.10 Dagbladenes Bureau interviewed me on using AI to hire people (Danish): Ansat af en maskine
2018.08 I was program co-chair for COLING 2018 (we had 1018 full paper submissions)
2018.07 Read an interview with me in "Alt om Data" (Danish): Nye sandheder om falske nyheder
brown (generalised)
\[ MI(C_i,C_j)= p(\left< C_i,C_j\right>)\ \log_2{\frac{p(\left<\ C_i,C_j\right>)}{p(\left< C_i,*\right>)\ p(\left<*,C_j\right>)}} \]
\[ AMI(C) = \sum_{C_i,C_j\in C}{MI(C_i,C_j)} \]
\[ C_{i\leftarrow j} = \left( C \setminus \left\{C_i,C_j\right\} \right) \cup \left\{C_i \cup C_j \right\} \]
\[ 0 < a \leq ||C|| \]
\[ i,j \in [1..a] \]
\[ \DeclareMathOperator*{\argmax}{arg\,max} \hat{\pi}(C) = \argmax_{C_i,C_j\in C,i\neq j}{\ AMI(C) - AMI(C_{i\leftarrow j})}. \]
NLP group at the ITU University of Copenhagen
1.5 permanent faculty
For details and research specialisations, visit the webpage: nlp.itu.dk
Natural Language Processing / Text Mining
Fake news, veracity & stance - how do we determine truth of claims on the web? What behaviours exist around false news? How do we know that the data your system is processing is geniune and accurate?
Noisy text processing - including social media text, and clinical text (medics' notoriously bad handwriting does, sometimes, map also to keyboard skills)
Clinical text mining - and pre-clinical public health. Continuing previous associations with Mayo Clinic, NHS SLaM, and Harvard Children's hospital
Information extraction - who did what to whom, and where and when? This is explained in the text but harder to automatically extract and reason about.
Danish NLP - Improve the places that you live in
keywords: natural language processing, machine learning, veracity, social media, semantics, artificial intelligence, dansk