{"created":"2020-09-01T12:31:53.981675+00:00","id":3450,"links":{},"metadata":{"_buckets":{"deposit":"1e42af75-844e-4e58-a092-8ee660548956"},"_deposit":{"id":"3450","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3450"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3450","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Clustering Analogous Words in Myanmar Language using Word Embedding Model","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Word embedding represents the words in terms ofvectors. It is influenced on different NLP researchareas such as document classification, authoridentification, sentiment analysis, etc. One of themost popular embedding models is Word2Vec model.It provides efficient representations of words by usingContinuous Bag of Words model (CBOW) and SkipGram model. In English language, word embeddingmodel can be applied for data preprocessing well butthere is a very little amount of work done inMyanmar language. Text preprocessing is importantpart to build embedding model and it is asignificantly effect on final results. This paper tries toextract the analogous words between Myanmar newsarticles focus on the bag of words (CBOW) modelusing different features vector sizes. By analyzingword embedding model are obtained the betterresults with a high dimensional vectors than a lowdimensional vectors to cluster the words based on itsrelatedness."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Word2Vec"},{"interim":"Continuous Bag of Words Model (CBOW)"},{"interim":"Myanmar Language"},{"interim":"Word Embedding"}]},"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-165-170.pdf","filesize":[{"value":"673 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3450/files/ICCA 2019 Proceedings Book-pages-165-170.pdf"},"version_id":"8ba49c07-05ef-4028-843e-952bf3f2369c"}]},"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":"Mon, Aye Myat"},{"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/123456789/1199"},"item_title":"Clustering Analogous Words in Myanmar Language using Word Embedding Model","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-23","publish_status":"0","recid":"3450","relation_version_is_last":true,"title":["Clustering Analogous Words in Myanmar Language using Word Embedding Model"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T00:34:16.527369+00:00"}