{"created":"2020-09-01T12:31:24.439699+00:00","id":3448,"links":{},"metadata":{"_buckets":{"deposit":"f4a98610-1bea-4977-9524-9b7cf8080579"},"_deposit":{"id":"3448","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3448"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3448","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Joint Word Segmentation and Part-of-Speech (POS) Tagging for Myanmar Language","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"In natural language processing (NLP), Wordsegmentation and Part-of-Speech (POS) tagging arefundamental tasks. The POS information is alsonecessary in NLP- based applications such asmachine translation (MT), information retrieval (IR),etc. Currently, there are many research efforts inword segmentation and POS tagging developedseparately with various approaches to reach highperformance and accuracy. For MyanmarLanguage, there are also separate word segmentorsand POS taggers based on statistical approachessuch as Neural Network (NN) and Hidden MarkovModels (HMMs). However, the Myanmar languagehas the complex morphological structure and theOut-of-Vocabulary (OOV) problem still exists. Thus,this paper proposed morphological analysis basedjoint Myanmar word segmentation and POS taggingusing Hidden Markov Models (HMM) andmorphological rules. This paper has also presentedthe comparison of accuracy resultusing HMM only, and HMM with morphologicalanalysis."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value":[]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-07-23"}],"displaytype":"preview","filename":"ICCA 2019 Proceedings Book-pages-152-157.pdf","filesize":[{"value":"866 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3448/files/ICCA 2019 Proceedings Book-pages-152-157.pdf"},"version_id":"680eeb30-66e6-48b8-8cc1-3a0e1fbcdc90"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Seventeenth International Conference on Computer Applications(ICCA 2019)","subitem_pages":"","subitem_volume":""}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"","subitem_c_date":"","subitem_conference_title":"","subitem_part":"","subitem_place":"","subitem_session":"","subitem_website":""}]},"item_1583103211336":{"attribute_name":"Books/reports/chapters","attribute_value_mlt":[{"subitem_book_title":"","subitem_isbn":"","subitem_pages":"","subitem_place":"","subitem_publisher":""}]},"item_1583103233624":{"attribute_name":"Thesis/dissertations","attribute_value_mlt":[{"subitem_awarding_university":"","subitem_supervisor(s)":[{"subitem_supervisor":""}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Cing, Dim Lam"},{"subitem_authors_fullname":"Soe, Khin Mar"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Article"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2019-02-27"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/1197"},"item_title":"Joint Word Segmentation and Part-of-Speech (POS) Tagging for Myanmar Language","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-23","publish_status":"0","recid":"3448","relation_version_is_last":true,"title":["Joint Word Segmentation and Part-of-Speech (POS) Tagging for Myanmar Language"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T06:09:01.860304+00:00"}