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The task entails prediction of the morphological tag (case, degree, gender, etc.) of an in-context word. We hypothesize that to predict the tag of a word, considering its longer context such as the entire sentence is not always necessary. In this light, the usefulness of convolution operation is studied resulting in a convolutional neural network (CNN) based morphological tagger. Our proposed model (BLSTM-CNN) achieves insightful results in comparison to the present state-of-the-art. Following the recent trend, the task is carried out under three different settings: single language, across languages, and across keys. Whereas the previous models used only character-level features, we show that the addition of word vectors along with character-level embedding significantly improves the performance of all the models. Since obtaining high-quality word vectors for resource-poor languages remains a challenge, in that scenario, the proposed character-level BLSTM-CNN proves to be most effective.\n            <jats:sup>1<\/jats:sup>\n          <\/jats:p>","DOI":"10.1145\/3342354","type":"journal-article","created":{"date-parts":[[2019,8,12]],"date-time":"2019-08-12T12:16:36Z","timestamp":1565612196000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["NeuMorph"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5947-4775","authenticated-orcid":false,"given":"Abhisek","family":"Chakrabarty","sequence":"first","affiliation":[{"name":"Indian Statistical Institute, Kolkata, West Bengal, India"}]},{"given":"Akshay","family":"Chaturvedi","sequence":"additional","affiliation":[{"name":"Indian Statistical Institute, Kolkata, West Bengal, India"}]},{"given":"Utpal","family":"Garain","sequence":"additional","affiliation":[{"name":"Indian Statistical Institute, Kolkata, West Bengal, India"}]}],"member":"320","published-online":{"date-parts":[[2019,8,10]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"15","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 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Celano Fabricio Chalub Jinho Choi \u00c7a\u01e7r\u0131 \u00c7\u00f6ltekin Miriam Connor Elizabeth Davidson Marie-Catherine de Marneffe Valeria de Paiva Arantza Diaz de Ilarraza Kaja Dobrovoljc Timothy Dozat Kira Droganova Puneet Dwivedi Marhaba Eli Toma\u017e Erjavec Rich\u00e1rd Farkas Jennifer Foster Cl\u00e1udia Freitas Katar\u00edna Gajdo\u0161ov\u00e1 Daniel Galbraith Marcos Garcia Filip Ginter Iakes Goenaga Koldo Gojenola Memduh G\u00f6k\u0131rmak Yoav Goldberg Xavier G\u00f3mez Guinovart Berta Gonz\u00e1les Saavedra Matias Grioni Normunds Gr\u016bz\u012btis Bruno Guillaume Nizar Habash Jan Haji\u010d Linh H\u00e0 M\u1ef9 Dag Haug Barbora Hladk\u00e1 Petter Hohle Radu Ion Elena Irimia Anders Johannsen Fredrik J\u00f8rgensen H\u00fcner Ka\u0131kara Hiroshi Kanayama Jenna Kanerva Natalia Kotsyba Simon Krek Veronika Laippala Ph\u01b0\u01a1ng L\u00ea H\u1ed3ng Alessandro Lenci Nikola Ljube\u0161i\u0107 Olga Lyashevskaya Teresa Lynn Aibek Makazhanov Christopher Manning C\u0103t\u0103lina M\u0103r\u0103nduc David Mare\u010dek H\u00e9ctor Mart\u00ednez Alonso Andr\u00e9 Martins Jan Ma\u0161ek Yuji Matsumoto Ryan McDonald Anna Missil\u00e4 Verginica Mititelu Yusuke Miyao Simonetta Montemagni Amir More Shunsuke Mori Bohdan Moskalevskyi Kadri Muischnek Nina Mustafina Kaili M\u00fc\u00fcrisep L\u01b0\u01a1ng Nguy\u1ec5n Th\u1ecb Huy\u1ec1n Nguy\u1ec5n Th\u1ecb Minh Vitaly Nikolaev Hanna Nurmi Stina Ojala Petya Osenova Lilja \u00d8vrelid Elena Pascual Marco Passarotti Cenel-Augusto Perez Guy Perrier Slav Petrov Jussi Piitulainen Barbara Plank Martin Popel Lauma Pretkalni\u0146a Prokopis Prokopidis Tiina Puolakainen Sampo Pyysalo Alexandre Rademaker Loganathan Ramasamy Livy Real Laura Rituma Rudolf Rosa Shadi Saleh Manuela Sanguinetti Baiba Saul\u012bte Sebastian Schuster Djam\u00e9 Seddah Wolfgang Seeker Mojgan Seraji Lena Shakurova Mo Shen Dmitry Sichinava Natalia Silveira Maria Simi Radu Simionescu Katalin Simk\u00f3 M\u00e1ria \u0160imkov\u00e1 Kiril Simov Aaron Smith Alane Suhr Umut Sulubacak Zsolt Sz\u00e1nt\u00f3 Dima Taji Takaaki Tanaka Reut Tsarfaty Francis Tyers Sumire Uematsu Larraitz Uria Gertjan van Noord Viktor Varga Veronika Vincze Jonathan North Washington Zden\u011bk \u017dabokrtsk\u00fd Amir Zeldes Daniel Zeman and Hanzhi Zhu. 2017. Universal Dependencies 2.0. http:\/\/hdl.handle.net\/11234\/1-1983 LINDAT\/CLARIN digital library at the Institute of Formal and Applied Linguistics Charles University."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2067"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.5120\/16683-6799"},{"volume-title":"Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing (FSMNLP\u201917)","author":"Ravishankar Vinit","key":"e_1_2_1_38_1","unstructured":"Vinit Ravishankar and Francis M. Tyers . 2017. Finite-state morphological analysis for Marathi . In Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing (FSMNLP\u201917) . 50--55. Vinit Ravishankar and Francis M. Tyers. 2017. Finite-state morphological analysis for Marathi. 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