Argumentation Mining in Scientific Literature for Sustainable Development

1 code implementation • EMNLP (ArgMining) 2021 • Aris Fergadis, Dimitris Pappas, Antonia Karamolegkou, Haris Papageorgiou

We also present a set of strong, BERT-based neural baselines achieving an f1-score of 70. 0 for Claim and 62. 4 for Evidence identification evaluated with 10-fold cross-validation.

SciNoBo : A Hierarchical Multi-Label Classifier of Scientific Publications

no code implementations • 2 Apr 2022 • Nikolaos Gialitsis, Sotiris Kotitsas, Haris Papageorgiou

Classifying scientific publications according to Field-of-Science (FoS) taxonomies is of crucial importance, allowing funders, publishers, scholars, companies and other stakeholders to organize scientific literature more effectively.

Protest Event Analysis: A Longitudinal Analysis for Greece

no code implementations • LREC 2020 • Konstantina Papanikolaou, Haris Papageorgiou

More specifically, the contribution of this work is to describe a Computational Social Science methodology for Event Analysis.

Research \& Innovation Activities' Impact Assessment: The Data4Impact System

no code implementations • LREC 2020 • Ioanna Grypari, Dimitris Pappas, Natalia Manola, Haris Papageorgiou

Cat.

BioRead: A New Dataset for Biomedical Reading Comprehension

1 code implementation • LREC 2018 • Dimitris Pappas, Ion Androutsopoulos, Haris Papageorgiou

Information Retrieval Machine Reading Comprehension +1

Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter

no code implementations • SEMEVAL 2017 • Athanasia Kolovou, Filippos Kokkinos, Aris Fergadis, Pinelopi Papalampidi, Elias Iosif, Mal, Nikolaos rakis, Elisavet Palogiannidi, Haris Papageorgiou, Shrikanth Narayanan, Alex Potamianos, ros

In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter.

General Classification Sentiment Analysis +1

Universal Dependencies for Greek

no code implementations • WS 2017 • Prokopis Prokopidis, Haris Papageorgiou

Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation

no code implementations • SEMEVAL 2016 • Elisavet Palogiannidi, Athanasia Kolovou, Fenia Christopoulou, Filippos Kokkinos, Elias Iosif, Mal, Nikolaos rakis, Haris Papageorgiou, Shrikanth Narayanan, Alex Potamianos, ros

Opinion Mining Twitter Sentiment Analysis +1

SemEval-2016 Task 5: Aspect Based Sentiment Analysis

no code implementations • SEMEVAL 2016 • Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Man, Suresh har, Mohammad AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orph{\'e}e De Clercq, V{\'e}ronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia Loukachevitch, Evgeniy Kotelnikov, Nuria Bel, Salud Mar{\'\i}a Jim{\'e}nez-Zafra, G{\"u}l{\c{s}}en Eryi{\u{g}}it

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

SemEval-2015 Task 12: Aspect Based Sentiment Analysis

no code implementations • SEMEVAL 2015 • Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Man, Suresh har, Ion Androutsopoulos

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)

Experiments for Dependency Parsing of Greek

no code implementations • WS 2014 • Prokopis Prokopidis, Haris Papageorgiou

Dependency Parsing

Entropy analysis of word-length series of natural language texts: Effects of text language and genre

no code implementations • 17 Jan 2014 • Maria Kalimeri, Vassilios Constantoudis, Constantinos Papadimitriou, Kostantinos Karamanos, Fotis K. Diakonos, Haris Papageorgiou

We estimate the $n$-gram entropies of natural language texts in word-length representation and find that these are sensitive to text language and genre.

Attribute

The META-SHARE Metadata Schema for the Description of Language Resources

no code implementations • LREC 2012 • Maria Gavrilidou, Penny Labropoulou, Elina Desipri, Stelios Piperidis, Haris Papageorgiou, Monica Monachini, Francesca Frontini, Thierry Declerck, Gil Francopoulo, Victoria Arranz, Valerie Mapelli

This paper presents a metadata model for the description of language resources proposed in the framework of the META-SHARE infrastructure, aiming to cover both datasets and tools/technologies used for their processing.

Machine Translation Retrieval