Leon Derczynski

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Leon Derczynski is a scientific Research Associate, who finished a PhD in Temporal Information Extraction at the University in Sheffield in 2012 under an enhanced EPSRC doctoral training grant. His main interests are information extraction, spatio-temporal semantics and handling noisy linguistic data. He has been working in commercial and academic research on NLP and IR since 2003 with focus on temporal relations, temporal and spatial information extraction, semantic annotation, usability and social media. He regularly guides teams through the TAC-KBP challenge, building complex information extraction and corroboration frameworks. Leon contributes to ISO consortiums for spatio-tempor

natural language processing computational linguistics social media linguistics nlp timeml fake news twitter information extraction artificial intelligence temporal expressions corpus linguistics crowdsourcing named entity recognition annotation machine learning misinformation false news sprogteknologi danish dansk demographics best practice data extraction linked data hypertext big data spatio-temporal database sentiment analysis future time and space agile software development question answering trec information retrieval relevance feedback wsd esslli word sense disambiguation past tense academic tense

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Leon Derczynski

Mining Social Media with Linked Open Data, Entity Recognition, and Event Extraction

Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data

TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text

Review of: Challenges of migrating to agile methodologies

Word Sense Disambiguation and Induction

Microblog-genre noise and its impact on semantic annotation accuracy

TIMEN: An Open Temporal Expression Normalisation Resource

Passive-Aggressive Sequence Labeling with Discriminative Post-Editing for Recognising Person Entities in Tweets

Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines

Towards Context-Aware Search and Analysis on Social Media Data

Empirical Validation of Reichenbach’s Tense Framework

A data driven approach to query expansion in question answering

Determining the Types of Temporal Relations in Discourse

Starting to Process Social Media

Recognising and Interpreting Named Temporal Expressions

Leveraging the Power of Social Media

An Annotation Scheme for Reichenbach's Verbal Tense Structure

RTMBank: Capturing Verbs with Reichenbach's Tense Model

Broad Twitter Corpus: A Diverse Named Entity Recognition Resource

Handling and Mining Linguistic Variation in UGC