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Word representations are formed by concatenating word and character embeddings with the morphological embeddings based on these schemes. The impact of these representations is measured by training and evaluating a sequential tagger composed of a conditional random field layer on top of a bidirectional long short-term memory layer. Experiments with Turkish, Czech, Hungarian, Finnish and Spanish produce the state-of-the-art results for all these languages, indicating that the representation of morphological information improves performance.<\/jats:p>","DOI":"10.1017\/s1351324918000281","type":"journal-article","created":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T05:51:27Z","timestamp":1532670687000},"page":"147-169","source":"Crossref","is-referenced-by-count":12,"title":["The effect of morphology in named entity recognition with sequence tagging"],"prefix":"10.1017","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7843-1439","authenticated-orcid":false,"given":"ONUR","family":"G\u00dcNG\u00d6R","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"TUNGA","family":"G\u00dcNG\u00d6R","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"SUZAN","family":"\u00dcSK\u00dcDARLI","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2018,7,27]]},"reference":[{"key":"S1351324918000281_ref036","unstructured":"Lee J. , Kim G. , Yoo J. , Jung C. , Kim M. , and Yoon S. 2017. 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