Search Results for author: Udo Kruschwitz

Found 30 papers, 7 papers with code

UR@NLP_A_Team @ GermEval 2021: Ensemble-based Classification of Toxic, Engaging and Fact-Claiming Comments

no code implementations GermEval 2021 Kwabena Odame Akomeah, Udo Kruschwitz, Bernd Ludwig

Exploring the issue of overfitting we uncovered that due to a bug in the pipeline the runs we submitted had not been trained on the full set but only on a small training set.

Exploring Fake News Detection with Heterogeneous Social Media Context Graphs

no code implementations13 Dec 2022 Gregor Donabauer, Udo Kruschwitz

Fake news detection has become a research area that goes way beyond a purely academic interest as it has direct implications on our society as a whole.

Fake News Detection Graph Classification

Aggregating Crowdsourced and Automatic Judgments to Scale Up a Corpus of Anaphoric Reference for Fiction and Wikipedia Texts

no code implementations11 Oct 2022 Juntao Yu, Silviu Paun, Maris Camilleri, Paloma Carretero Garcia, Jon Chamberlain, Udo Kruschwitz, Massimo Poesio

Although several datasets annotated for anaphoric reference/coreference exist, even the largest such datasets have limitations in terms of size, range of domains, coverage of anaphoric phenomena, and size of documents included.

A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts

1 code implementation LREC 2022 Miriam Schirmer, Udo Kruschwitz, Gregor Donabauer

Recent progress in natural language processing has been impressive in many different areas with transformer-based approaches setting new benchmarks for a wide range of applications.

Transfer Learning

ur-iw-hnt at GermEval 2021: An Ensembling Strategy with Multiple BERT Models

1 code implementation GermEval 2021 Hoai Nam Tran, Udo Kruschwitz

This paper describes our approach (ur-iw-hnt) for the Shared Task of GermEval2021 to identify toxic, engaging, and fact-claiming comments.

Interactive query expansion for professional search applications

1 code implementation25 Jun 2021 Tony Russell-Rose, Philip Gooch, Udo Kruschwitz

Knowledge workers (such as healthcare information professionals, patent agents and recruitment professionals) undertake work tasks where search forms a core part of their duties.

A Mention-Pair Model of Annotation with Nonparametric User Communities

no code implementations25 Sep 2019 Silviu Paun, Juntao Yu, Jon Chamberlain, Udo Kruschwitz, Massimo Poesio

The model is also flexible enough to be used in standard annotation tasks for classification where it registers on par performance with the state of the art.

Crowdsourcing and Aggregating Nested Markable Annotations

1 code implementation ACL 2019 Chris Madge, Juntao Yu, Jon Chamberlain, Udo Kruschwitz, Silviu Paun, Massimo Poesio

One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation.

coreference-resolution Coreference Resolution +2

A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation

no code implementations NAACL 2019 Massimo Poesio, Jon Chamberlain, Silviu Paun, Juntao Yu, Alex Uma, ra, Udo Kruschwitz

The corpus, containing annotations for about 108, 000 markables, is one of the largest corpora for coreference for English, and one of the largest crowdsourced NLP corpora, but its main feature is the large number of judgments per markable: 20 on average, and over 2. 2M in total.

Information search in a professional context - exploring a collection of professional search tasks

no code implementations11 May 2019 Suzan Verberne, Jiyin He, Gineke Wiggers, Tony Russell-Rose, Udo Kruschwitz, Arjen P. de Vries

Search conducted in a work context is an everyday activity that has been around since long before the Web was invented, yet we still seem to understand little about its general characteristics.

Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games

no code implementations ICLR 2018 Dino S. Ratcliffe, Luca Citi, Sam Devlin, Udo Kruschwitz

Many deep reinforcement learning approaches use graphical state representations, this means visually distinct games that share the same underlying structure cannot effectively share knowledge.

Domain Adaptation Multi-Task Learning +2

Comparing Bayesian Models of Annotation

no code implementations TACL 2018 Silviu Paun, Bob Carpenter, Jon Chamberlain, Dirk Hovy, Udo Kruschwitz, Massimo Poesio

We evaluate these models along four aspects: comparison to gold labels, predictive accuracy for new annotations, annotator characterization, and item difficulty, using four datasets with varying degrees of noise in the form of random (spammy) annotators.

Model Selection

Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.

no code implementations LREC 2016 Jon Chamberlain, Massimo Poesio, Udo Kruschwitz

Corpora are typically annotated by several experts to create a gold standard; however, there are now compelling reasons to use a non-expert crowd to annotate text, driven by cost, speed and scalability.

text annotation

The OnForumS corpus from the Shared Task on Online Forum Summarisation at MultiLing 2015

no code implementations LREC 2016 Mijail Kabadjov, Udo Kruschwitz, Massimo Poesio, Josef Steinberger, Jorge Valderrama, Hugo Zaragoza

In this paper we present the OnForumS corpus developed for the shared task of the same name on Online Forum Summarisation (OnForumS at MultiLing{'}15).

Towards a Corpus of Violence Acts in Arabic Social Media

no code implementations LREC 2016 Ayman Alhelbawy, Poesio Massimo, Udo Kruschwitz

In this paper we present a new corpus of Arabic tweets that mention some form of violent event, developed to support the automatic identification of Human Rights Abuse.

Combining Minimally-supervised Methods for Arabic Named Entity Recognition

no code implementations TACL 2015 Maha Althobaiti, Udo Kruschwitz, Massimo Poesio

Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change.

named-entity-recognition Named Entity Recognition +2

Applying Random Indexing to Structured Data to Find Contextually Similar Words

no code implementations LREC 2012 Danica Damljanovi{\'c}, Udo Kruschwitz, M-Dyaa Albakour, Johann Petrak, Mihai Lupu

Our approach is based on exploiting the structure inherent in an RDF graph and then applying the methods from statistical semantics, and in particular, Random Indexing, in order to discover contextually related terms.

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