Anders Søgaard

Research output

Research output per year 2004 2007 2009 2014 2015 2016 2017 2018 2019

• 141 Article in proceedings
• 13 Journal article
• 8 Book chapter
• 5 Report chapter
• 19 More
  • 4 Review
  • 3 Paper
  • 2 Book
  • 2 Preface/postscript
  • 2 Conference article
  • 1 Anthology
  • 1 Doctoral thesis
  • 1 Ph.D. thesis
  • 1 Conference abstract for conference
  • 1 Poster
  • 1 Conference abstract in journal

Research output per year

Research output per year

Search results

• 2019

  Adversarial Removal of Demographic Attributes Revisited

  Barrett, M., Kementchedjhieva, Y., Elazar, Y., Elliott, D. & Søgaard, A., 1 Nov 2019, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong, China: Association for Computational Linguistics (ACL), p. 6329-6334 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
• 2018

  A Danish FrameNet Lexicon and an Annotated Corpus Used for Training and Evaluating a Semantic Frame Classifier

  Pedersen, B. S., Nimb, S., Søgaard, A., Hartmann, M. & Olsen, S., 2018, Proceedings of the 11th edition of the Language Resources and Evaluation Conference, Miyazaki, Japan.. European Language Resources Association, 5 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• A Discriminative Latent-Variable Model for Bilingual Lexicon Induction

  Ruder, S., Cotterell, R., Kementchedjhieva, Y. R. & Søgaard, A., 2018, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, p. 458–468

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Bilingual Lexicon Induction 100%
  • Latent Variable Model 100%
  • Hubness Problem 33%

• A strong baseline for question relevancy ranking

  Gonzalez, A. V., Augenstein, I. & Søgaard, A., 2018, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, p. 4810–4815

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Character-level Supervision for Low-resource POS Tagging

  Kann, K., Bjerva, J., Augenstein, I., Plank, B. & Søgaard, A., 2018, Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP. Association for Computational Linguistics, p. 1–11

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Generalizing Procrustes Analysis for Better Bilingual Dictionary Induction

  Kementchedjhieva, Y. R., Ruder, S., Cotterell, R. & Søgaard, A., 2018, Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). Association for Computational Linguistics, p. 211–220

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  15 Citations (Scopus)

• Leaming LanguageJndepondent Roprcsentations of Vorbe snd AdjcctivEs from Multimodal Retricvsl

  Hansen, V. P. B. & Søgaard, A., 2018, Proceedings of the 14th lnternatlonal Conference on Signal lmage Technology & lnternet Based Systems: SITIS 2018. IEEE, p. 427-434

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Learning Language-Independent Representations of Verbs and Adjectives from Multimodal Retrieval

  Hansen, V. P. B. & Sogaard, A., 2 Jul 2018, Proceedings - 14th International Conference on Signal-Image Technology and Internet Based Systems, SITIS. IEEE, p. 427-434 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Search Engine 100%
  • Language Development 100%
  • image feature 100%
  • Image Search 100%
  • Information Retrieval 100%

• Learning to predict readability using eye-movement data from natives and learners

  González-Garduño, A. V. & Søgaard, A., 2018, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, Proceedings. AAAI Press, p. 5118-5124 7 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Language Learner 100%
  • AI Models 33%
  • Assisted Technology 16%
  • Gaze Information 16%
  • Learner Difficulties 16%
  6 Citations (Scopus)

• Lexii: A tool for adaptive, personalized text simplification

  Bingel, J., Paetzold, G. H. & Søgaard, A., 2018, Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, p. 245-258

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Limitations of cross-lingual learning from image search

  Hartmann, M. & Søgaard, A., 2018, Proceedings of the 3rd Workshop on Representation Learning for NLP. p. 59–163

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Linguistic representations in multi-task neural networks for ellipsis resolution

  Rønning, O., Hardt, D. & Søgaard, A., 2018, Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Association for Computational Linguistics, p. 66–73

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Multi-task learning for historical text normalization: Size matters

  Bollmann, M. M., Søgaard, A. & Bingel, J., 2018, Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP. Association for Computational Linguistics, p. 19–24

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces

  Augenstein, I., Ruder, S. & Søgaard, A., 2018, Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : (Long Papers). Association for Computational Linguistics, Vol. 1. p. 1896–1906 11 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Sequence Classification 100%
  • Classification Task 100%
  • Multitask Learning 100%
  • Label Space 100%
  • Sentiment Analysis 50%
  21 Citations (Scopus)

  72 Downloads (Pure)

• Nightmare at test time: How punctuation prevents parsers from generalizing

  Søgaard, A., Lhoneux, M. D. & Augenstein, I., 2018, Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP . Association for Computational Linguistics, p. 25–29

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• On the Limitations of Unsupervised Bilingual Dictionary Induction

  Søgaard, A., Ruder, S. & Vulic, I., 2018, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: (Long papers). Association for Computational Linguistics, p. 778–788

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  59 Citations (Scopus)

• Parameter sharing between dependency parsers for related languages

  Lhoneux, M. D., Bjerva, J., Augenstein, I. & Søgaard, A., 2018, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Parameter Sharing 100%
  • Related Language 100%
  • Unrelated Languages 66%
  • Character Parameter 33%
  • Shared Strategy 33%

• Sentiment analysis under temporal shift

  Lukes, J. & Søgaard, A., 2018, Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics, p. 65–71

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Sequence classification with human attention

  Barrett, M. J., Bingel, J., Hollenstein, N., Rei, M. & Søgaard, A., 2018, Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018). Korhonen , A. & Titov , I. (eds.). Association for Computational Linguistics, p. 302–312

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Human Attention 100%
  • Sequence Classification 100%
  • Sentiment Analysis 100%
  • Recurrent Neural Network 100%
  • Opinion Mining 100%
  22 Citations (Scopus)

  1 Downloads (Pure)

• Sluice resolution without hand-crafted features over brittle syntax trees

  Rønning, O., Hardt, D. & Søgaard, A., 2018, Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : (Long Papers). Association for Computational Linguistics, Vol. 1. p. 236–241

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing

  Barrett, M. J., Gonzalez, A. V., Frermann, L. & Søgaard, A., 2018, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): Human Language Technologies, (Long Papers) . Cordeiro , S. R., Oraby, S., Pavalanathan, U. & Rim, K. (eds.). Association for Computational Linguistics, Vol. 1. p. 2028-2038 11 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Dictionary 100%
  • Syntactics 100%
  • Word Embedding 100%
  • Error Reduction 66%
  • Data Source 50%
  1 Citation (Scopus)

• What I think when I think about treebanks

  Søgaard, A., 2018, Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories (TLT16),. Association for Computational Linguistics, p. 161-166

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• When does deep multi-task learning work for loosely related document classification tasks?

  Kerinec, E., Søgaard, A. & Braud, C., 2018, Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Association for Computational Linguistics, p. 1-8

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Why is unsupervised alignment of English embeddings from different algorithms so hard?

  Hartmann, M., Kementchedjhieva, Y. R. & Søgaard, A., 2018, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, p. 582–586

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens

  Rei, M. & Søgaard, A., 2018, Proceedings, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : (Long Ppaers). Association for Computational Linguistics, Vol. 1. p. 293–302

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Knowledge Transfer 100%
  • Token 100%
  • Sequence Labeling 100%
  • Binary Sequence 100%
  • Trained Network 100%
  16 Citations (Scopus)
• 2017

  A strong baseline for learning cross-lingualword embeddings from sentence alignments

  Levy, O., Søgaard, A. & Goldberg, Y., 2017, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Long papers. Association for Computational Linguistics, Vol. 1. p. 765-774 10 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Sentence Alignment 100%
  • Cross-lingual Word Embeddings 100%
  • Aligned Corpus 50%
  • Embedding Algorithm 50%
  • Alignment Algorithm 50%
  33 Citations (Scopus)

• Cross-lingual and cross-domain discourse segmentation of entire documents

  Braud, C., Lacroix, O. & Søgaard, A., 1 Jan 2017, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Short papers. Association for Computational Linguistics, Vol. 2. p. 237-243 7 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Cross-lingual 100%
  • Discourse Segmentation 100%
  • Cross-domain 100%
  • Syntactics 100%
  • Gold Standard 100%
  7 Citations (Scopus)

• Cross-lingual RST discourse parsing

  Braud, C. E., Coavoux, M. & Søgaard, A., 2017, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: long papers. Association for Computational Linguistics, Vol. 1. p. 292-304 13 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Discourse 100%
  • Parsing 100%
  • Discourse Treebank 75%
  • Treebank 57%
  • English 28%
  17 Citations (Scopus)

• Cross-lingual tagger evaluation without test data

  Agic, Z., Plank, B. & Søgaard, A., 2017, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: short papers. Association for Computational Linguistics, Vol. 2. p. 248-253 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Cross-lingual 100%
  • Low-resource Languages 100%
  • Prediction Accuracy 50%
  • Dictionary-based 50%
  • POS Tagging 50%
  4 Citations (Scopus)

• Does syntax help discourse segmentation? Not so much

  Braud, C. E., Lacroix, O. & Søgaard, A., 2017, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, p. 2432–2442 11 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Discourse Parsing 100%
  • Discourse Segmentation 100%
  • Gold Standard 100%
  • Sentence Boundary 100%
  • Parsing 100%
  5 Citations (Scopus)

• Evaluating hypotheses in geolocation on a very large sample of Twitter

  Salehi, B. & Søgaard, A., 2017, Proceedings of the 3rd Workshop on Noisy User-generated Text. Association for Computational Linguistics, p. 62-67 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Named Entity 100%
  • Twitter 100%
  • Geotagged Tweets 50%
  • Geotag 50%

• From Thesaurus to Framenet

  Nimb, S., Braasch, A., Olsen, S., Pedersen, B. S. & Søgaard, A., 2017, Electronic Lexicography in the 21st Century : Proceedings of eLex 2017 conference. Kosem, I., Tiberius, C., Jabobicek, M., Kallas, J., Krej, S. & Baisa, V. (eds.). Lexical Computing CZ, p. 1-22 23 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Thesaurus 100%
  • Linguistics 100%
  • English 100%
  • Dictionary 66%
  • Lexicon 66%
  50 Downloads (Pure)

• Huntsville, hospitals, and hockey teams: Names can reveal your location

  Salehi, B., Hovy, D., Hovy, E. & Søgaard, A., 2017, Proceedings of the 3rd Workshop on Noisy User-generated Text. Association for Computational Linguistics, p. 161-121

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

• Identifying beneficial task relations for multi-task learning in deep neural networks

  Bingel, J. & Søgaard, A., 2017, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: volume 2, short papers. Association for Computational Linguistics, Vol. 2. p. 164-169 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Multi-task Learning 100%
  • Deep Neural Network 100%
  • Multitask Learning 100%
  • Learning Task 100%
  • Neural Network 100%
  49 Citations (Scopus)

• Is writing style predictive of scientific fraud?

  Braud, C. E. & Søgaard, A., 2017, Proceedings of the Workshop on Stylistic VariationAssociation for Computational Linguistics. Association for Computational Linguistics, p. 37-42 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Writing Style 100%
  • Writing style 100%
  • Scientific fraud 100%
  • Machine Learning 100%
  • Hedging 100%

• Learning attention for historical text normalization by learning to pronounce

  Bollmann, M., Bingel, J. & Søgaard, A., 1 Jan 2017, ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). Association for Computational Linguistics, p. 332-344 13 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Learning Attention 100%
  • Text Normalization 100%
  • Grapheme-to-phoneme 33%
  • Focus Attention 33%
  9 Citations (Scopus)

• Learning what to share between loosely related tasks

  Ruder, S., Bingel, J., Augenstein, I. & Søgaard, A., 23 May 2017, In: arXiv.

  Research output: Contribution to journal › Journal article › Research

  • Multitask Learning 100%
  • Neural Network 100%
  • Knowledge Transfer 33%
  • Perform Experiment 33%
  • Deep Neural Network 33%
  17 Downloads (Pure)

• Multi-Task Learning of Keyphrase Boundary Classification

  Augenstein, I. & Søgaard, A., 2017, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics : (Short Papers). Barzilay, R. & Kan, M.-Y. (eds.). Association for Computational Linguistics, Vol. 2. p. 341-346 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Boundary Classification 100%
  • Auxiliary 100%
  • Recurrent Neural Network 100%
  • Marking 100%
  • State of the Art 100%
  20 Citations (Scopus)

• Parsing Universal Dependencies without training

  Martinez Alonso, H., Agic, Z., Plank, B. & Søgaard, A., 2017, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: long papers. Association for Computational Linguistics, Vol. 1. p. 230-240 11 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Parsing 100%
  • Leaf Nodes 33%
  • Training-free 33%
  7 Citations (Scopus)

• Spikes as Regularizers

  Søgaard, A., 2017, ESANN 2017 - Proceedings: 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN , p. 371-376 6 p. (arXiv.org).

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Layer Feed 100%
  • Adaptive Update 100%
  • Confidence Estimation 100%
  • Temporal Precision 100%
  • Confidence-based 100%

• Using gaze to predict text readability

  Gonzalez, A. V. & Søgaard, A., 2017, Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, p. 438-443

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Text Readability 100%
  • Neural Network 100%
  • Logistic Regression Model 100%
  • Hard Parameter Sharing 33%
  • Single-task Learning 33%

• Using hyperlinks to improve multilingual partial parsers

  Søgaard, A., 2017, Proceedings of the 15th International Conference on Parsing Technologies. Association for Computational Linguistics, p. 67-71

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Multilingualism 100%
  • English 100%
  • Syntax 100%
  • Natural Language Processing 50%
  • Syntactic Constituent 25%

• Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

  Felbo, B., Mislove, A., Søgaard, A., Rahwan, I. & Lehmann, S., 2017, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, p. 615–1625 11 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Domain Representation 100%
  • Emotions Sentiments 50%
  • Emotional Labels 50%
  • Emoji Prediction 50%
  144 Citations (Scopus)
• 2016

  A test suite for evaluating POS taggers across varieties of English

  Jørgensen, A. & Søgaard, A., 11 Apr 2016, Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee, p. 615-618 4 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • English Varieties 100%
  • Taggers 100%
  • English 100%
  • Twitter 50%
  • Hip-hop 50%

• Biases we live by

  Søgaard, A., 2016, In: Nordisk Tidsskrift for Informationsvidenskab og Kulturformidling. 5, 1, p. 31-35 5 p.

  Research output: Contribution to journal › Journal article › Research › peer-review

• Cross-lingual transfer of correlations between parts of speech and gaze features

  Barrett, M. J., Keller, F. & Søgaard, A., 2016, The 26th International Conference on Computational Linguistics (COLING): proceedings of COLING 2016: technical papers. p. 1330-1339 10 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • NLP Model 100%
  • Part-of-speech Features 100%
  • Cross-lingual Transfer 100%
  • Eye Movement 100%
  • Tagging 100%
  8 Citations (Scopus)

  93 Downloads (Pure)

• Deep multi-task learning with low level tasks supervised at lower layers

  Søgaard, A. & Goldberg, Y., 2016, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Vol. 2. p. 231-235 5 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Deep multi-task Learning 100%
  • Multitask Learning 100%
  • Shared Representations 50%
  • CCG Supertagging 50%
  • Learning Architecture 50%
  173 Citations (Scopus)

• Evaluating word embeddings with fMRI and eye-tracking

  Søgaard, A., 2016, Proceedings of the 1st Workshop on Evaluating Vector Space Representations for NLP. Association for Computational Linguistics, p. 116-121 6 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Eye Tracking 100%
  • Word Embedding 100%
  • Functional Magnetic Resonance Imaging 100%
  • Evaluation Metrics 25%
  • Aggregate Statistics 25%

• Extracting token-level signals of syntactic processing from fMRI - with an application to PoS induction

  Bingel, J., Barrett, M. J. & Søgaard, A., 2016.

  Research output: Contribution to conference › Paper › Research › peer-review

  • Syntactic Processing 100%
  • tokens 100%
  • Part of speech 100%
  • Temporal Resolution 100%
  • Level Signal 100%
  3 Citations (Scopus)

• Improving historical spelling normalization with bi-directional LSTMs and multi-task learning

  Bollman, M. & Søgaard, A., 2016, The 26th International Conference on Computational Linguistics: proceedings of COLING 2016: technical Papers. p. 131-139 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Spelling Normalization 100%
  • Multi-task Learning 100%
  • Bidirectional Long Short-term Memory (BiLSTM) 100%
  • Bidirectional Long Short-Term Memory Network 100%
  • Multitask Learning 100%
  19 Citations (Scopus)

  59 Downloads (Pure)