Search Results for author: Johann Petrak

Found 7 papers, 0 papers with code

Misogyny classification of German newspaper forum comments

no code implementations30 Nov 2022 Johann Petrak, Brigitte Krenn

We describe the creation of a corpus of 6600 comments which were annotated with 5 levels of misogyny.


Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus

no code implementations5 Jun 2020 Xingyi Song, Johann Petrak, Ye Jiang, Iknoor Singh, Diana Maynard, Kalina Bontcheva

The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide.

Fact Checking General Classification

A Deep Neural Network Sentence Level Classification Method with Context Information

no code implementations EMNLP 2018 Xingyi Song, Johann Petrak, Angus Roberts

In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification.

Classification General Classification +2

An Extensible Multilingual Open Source Lemmatizer

no code implementations RANLP 2017 Ahmet Aker, Johann Petrak, Firas Sabbah

The performance drops when there is no support from HFST and the entire lemmatization process is based on lemma dictionaries.

Information Retrieval LEMMA +1

Analysis of Named Entity Recognition and Linking for Tweets

no code implementations27 Oct 2014 Leon Derczynski, Diana Maynard, Giuseppe Rizzo, Marieke van Erp, Genevieve Gorrell, Raphaël Troncy, Johann Petrak, Kalina Bontcheva

Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area.

Entity Disambiguation Language Identification +4

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.

Question Answering

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