no code implementations • CLIB 2022 • Silvia Gargova, Irina Temnikova, Ivo Dzhumerov, Hristiana Nikolaeva
The article presents the manual annotation procedure of the first dataset, a dis- cussion of the decisions of the two annotators, and the results from testing the 7 off-the-shelf LI tools on both datasets.
no code implementations • RANLP 2019 • Irina Temnikova, Ahmed Abdelali, Souhila Djabri, Samy Hedaya
We analyze several speakers and interpreters variables via manual annotation and automatic methods.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
2 code implementations • IJCNLP 2019 • Prathyusha Jwalapuram, Shafiq Joty, Irina Temnikova, Preslav Nakov
The ongoing neural revolution in machine translation has made it easier to model larger contexts beyond the sentence-level, which can potentially help resolve some discourse-level ambiguities such as pronominal anaphora, thus enabling better translations.
no code implementations • RANLP 2017 • Irina Temnikova, Ahmed Abdelali, Samy Hedaya, Stephan Vogel, Aishah Al Daher
In this article we run an automatic analysis of a corpus of parallel speeches and their human interpretations, and provide the results of manually annotating the human interpreting strategies in a sample of the corpus.
no code implementations • 9 Oct 2016 • Ahmad Musleh, Nadir Durrani, Irina Temnikova, Preslav Nakov, Stephan Vogel, Osama Alsaad
We present research towards bridging the language gap between migrant workers in Qatar and medical staff.
no code implementations • LREC 2016 • Victoria Yaneva, Irina Temnikova, Ruslan Mitkov
This paper presents an approach for automatic evaluation of the readability of text simplification output for readers with cognitive disabilities.
no code implementations • LREC 2016 • Victoria Yaneva, Irina Temnikova, Ruslan Mitkov
This division of the groups informs researchers on whether particular fixations were elicited from skillful or less-skillful readers and allows a fair between-group comparison for two levels of reading ability.
no code implementations • LREC 2016 • K. Bretonnel Cohen, William A. Baumgartner Jr., Irina Temnikova
This paper reports SuperCAT, a corpus analysis toolkit.
no code implementations • LREC 2016 • Irina Temnikova, Wajdi Zaghouani, Stephan Vogel, Nizar Habash
The goal of the cognitive machine translation (MT) evaluation approach is to build classifiers which assign post-editing effort scores to new texts.
no code implementations • LREC 2014 • Irina Temnikova, William A. Baumgartner Jr., Negacy D. Hailu, Ivelina Nikolova, Tony McEnery, Adam Kilgarriff, Galia Angelova, K. Bretonnel Cohen
SubCAT, the Sublanguage Corpus Analysis Toolkit, assesses the representativeness and closure properties of corpora to analyze the extent to which they are either sublanguages, or representative samples of the general language.
no code implementations • LREC 2014 • Irina Temnikova, Andrea Varga, Dogan Biyikli
Extracting information from social media is being currently exploited for a variety of tasks, including the recognition of emergency events in Twitter.
no code implementations • LREC 2012 • Irina Temnikova, Constantin Orasan, Ruslan Mitkov
This article presents a new linguistic resource in the form of Controlled Language (CL) guidelines for manual text simplification in the CM domain which aims to address high TC in the CM domain and produce clear messages to be used in crisis situations.