{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-named-entity-recognition-by-jointly","title":"Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags","arxiv_id":"1807.06683","date":"2018-07-17","proceeding":null,"authors":["Onur Güngör","Suzan Üsküdarlı","Tunga Güngör"],"abstract":"Previous studies have shown that linguistic features of a word such as\npossession, genitive or other grammatical cases can be employed in word\nrepresentations of a named entity recognition (NER) tagger to improve the\nperformance for morphologically rich languages. However, these taggers require\nexternal morphological disambiguation (MD) tools to function which are hard to\nobtain or non-existent for many languages. In this work, we propose a model\nwhich alleviates the need for such disambiguators by jointly learning NER and\nMD taggers in languages for which one can provide a list of candidate\nmorphological analyses. We show that this can be done independent of the\nmorphological annotation schemes, which differ among languages. Our experiments\nemploying three different model architectures that join these two tasks show\nthat joint learning improves NER performance. Furthermore, the morphological\ndisambiguator's performance is shown to be competitive.","url_abs":"http://arxiv.org/abs/1807.06683v1","url_pdf":"http://arxiv.org/pdf/1807.06683v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"improving-named-entity-recognition-by-jointly","repo_url":"https://github.com/onurgu/joint-ner-and-md-tagger","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"morphological-disambiguation","task_name":"Morphological Disambiguation"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.06683","atlas_url":"https://app.syntology.ai/?focus=1807.06683","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}