A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Standard

A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment. / Ahmadi, Sina; McCrae, John; Nimb, Sanni; Khan, Fahad; Monachini, Monica; Pedersen, Bolette Sandford; Declerck, Thierry; Wissik, Tanja; Bellandi, Andrea; Pisani, Irene; Troelsgård, Thomas; Olsen, Sussi; Krek, Simon; Lipp, Veronika; Váradi, Tamás; Simon, László; Gyorffy, András; Tiberius, Carole; Schoonheim, Tanneke; Moshe, Yifat Ben; Rudich, Maya; Abu Ahmad, Raya; Lonke, Dorielle; Kovalenko, Kira; Langemets, Margit; Kallas, Jelena; Dereza, Oksana; Fransen, Theodorus; Cillessen, David; Lindemann, David; Alonso, Mikel; Salgado, Ana; Sancho, José Luis; Ureña-Ruiz, Rafael-J; Zamorano, Jordi Porta; Simov, Kiril; Osenova, Petya; Kancheva, Zara; Radev, Ivaylo; Stanković, Ranka; Perdih, Andrej; Gabrovsek, Dejan.

Proceedings of the 12th Language Resources and Evaluation Conference. Marseille, France : European Language Resources Association, 2020. p. 3232-3242.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Harvard

Ahmadi, S, McCrae, J, Nimb, S, Khan, F, Monachini, M, Pedersen, BS, Declerck, T, Wissik, T, Bellandi, A, Pisani, I, Troelsgård, T, Olsen, S, Krek, S, Lipp, V, Váradi, T, Simon, L, Gyorffy, A, Tiberius, C, Schoonheim, T, Moshe, YB, Rudich, M, Abu Ahmad, R, Lonke, D, Kovalenko, K, Langemets, M, Kallas, J, Dereza, O, Fransen, T, Cillessen, D, Lindemann, D, Alonso, M, Salgado, A, Sancho, JL, Ureña-Ruiz, R-J, Zamorano, JP, Simov, K, Osenova, P, Kancheva, Z, Radev, I, Stanković, R, Perdih, A & Gabrovsek, D 2020, A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment. in Proceedings of the 12th Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, pp. 3232-3242.

APA

Ahmadi, S., McCrae, J., Nimb, S., Khan, F., Monachini, M., Pedersen, B. S., ... Gabrovsek, D. (2020). A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment. In Proceedings of the 12th Language Resources and Evaluation Conference (pp. 3232-3242). Marseille, France: European Language Resources Association.

Vancouver

Ahmadi S, McCrae J, Nimb S, Khan F, Monachini M, Pedersen BS et al. A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment. In Proceedings of the 12th Language Resources and Evaluation Conference. Marseille, France: European Language Resources Association. 2020. p. 3232-3242

Author

Ahmadi, Sina ; McCrae, John ; Nimb, Sanni ; Khan, Fahad ; Monachini, Monica ; Pedersen, Bolette Sandford ; Declerck, Thierry ; Wissik, Tanja ; Bellandi, Andrea ; Pisani, Irene ; Troelsgård, Thomas ; Olsen, Sussi ; Krek, Simon ; Lipp, Veronika ; Váradi, Tamás ; Simon, László ; Gyorffy, András ; Tiberius, Carole ; Schoonheim, Tanneke ; Moshe, Yifat Ben ; Rudich, Maya ; Abu Ahmad, Raya ; Lonke, Dorielle ; Kovalenko, Kira ; Langemets, Margit ; Kallas, Jelena ; Dereza, Oksana ; Fransen, Theodorus ; Cillessen, David ; Lindemann, David ; Alonso, Mikel ; Salgado, Ana ; Sancho, José Luis ; Ureña-Ruiz, Rafael-J ; Zamorano, Jordi Porta ; Simov, Kiril ; Osenova, Petya ; Kancheva, Zara ; Radev, Ivaylo ; Stanković, Ranka ; Perdih, Andrej ; Gabrovsek, Dejan. / A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment. Proceedings of the 12th Language Resources and Evaluation Conference. Marseille, France : European Language Resources Association, 2020. pp. 3232-3242

Bibtex

@inproceedings{a675decdf072459481b10e42e227762b,
title = "A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment",
abstract = "Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment iscarried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships suchas broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide rangeof languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data willpave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriouslyrequiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.",
author = "Sina Ahmadi and John McCrae and Sanni Nimb and Fahad Khan and Monica Monachini and Pedersen, {Bolette Sandford} and Thierry Declerck and Tanja Wissik and Andrea Bellandi and Irene Pisani and Thomas Troelsg{\aa}rd and Sussi Olsen and Simon Krek and Veronika Lipp and Tam{\'a}s V{\'a}radi and L{\'a}szl{\'o} Simon and Andr{\'a}s Gyorffy and Carole Tiberius and Tanneke Schoonheim and Moshe, {Yifat Ben} and Maya Rudich and {Abu Ahmad}, Raya and Dorielle Lonke and Kira Kovalenko and Margit Langemets and Jelena Kallas and Oksana Dereza and Theodorus Fransen and David Cillessen and David Lindemann and Mikel Alonso and Ana Salgado and Sancho, {Jos{\'e} Luis} and Rafael-J Ure{\~n}a-Ruiz and Zamorano, {Jordi Porta} and Kiril Simov and Petya Osenova and Zara Kancheva and Ivaylo Radev and Ranka Stanković and Andrej Perdih and Dejan Gabrovsek",
year = "2020",
language = "English",
pages = "3232--3242",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
publisher = "European Language Resources Association",

}

RIS

TY - GEN

T1 - A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment

AU - Ahmadi, Sina

AU - McCrae, John

AU - Nimb, Sanni

AU - Khan, Fahad

AU - Monachini, Monica

AU - Pedersen, Bolette Sandford

AU - Declerck, Thierry

AU - Wissik, Tanja

AU - Bellandi, Andrea

AU - Pisani, Irene

AU - Troelsgård, Thomas

AU - Olsen, Sussi

AU - Krek, Simon

AU - Lipp, Veronika

AU - Váradi, Tamás

AU - Simon, László

AU - Gyorffy, András

AU - Tiberius, Carole

AU - Schoonheim, Tanneke

AU - Moshe, Yifat Ben

AU - Rudich, Maya

AU - Abu Ahmad, Raya

AU - Lonke, Dorielle

AU - Kovalenko, Kira

AU - Langemets, Margit

AU - Kallas, Jelena

AU - Dereza, Oksana

AU - Fransen, Theodorus

AU - Cillessen, David

AU - Lindemann, David

AU - Alonso, Mikel

AU - Salgado, Ana

AU - Sancho, José Luis

AU - Ureña-Ruiz, Rafael-J

AU - Zamorano, Jordi Porta

AU - Simov, Kiril

AU - Osenova, Petya

AU - Kancheva, Zara

AU - Radev, Ivaylo

AU - Stanković, Ranka

AU - Perdih, Andrej

AU - Gabrovsek, Dejan

PY - 2020

Y1 - 2020

N2 - Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment iscarried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships suchas broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide rangeof languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data willpave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriouslyrequiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.

AB - Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment iscarried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships suchas broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide rangeof languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data willpave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriouslyrequiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.

M3 - Article in proceedings

SP - 3232

EP - 3242

BT - Proceedings of the 12th Language Resources and Evaluation Conference

PB - European Language Resources Association

CY - Marseille, France

ER -

ID: 241583424