{"_buckets": {"deposit": "d24425da-272c-49fe-bd1c-8832adf49230"}, "_deposit": {"created_by": 45, "id": "6851", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "6851"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00006851", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Comparative Analysis of Deep Learning Models for Myanmar Text Classification", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Text classification is one of the important research areas in Natural Language Processing (NLP). Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM) and their combination models have been applied in many NLP tasks. In this paper, we present a joint CNN with no max-polling layer and Bidirectional LSTM to fulfill the requirements of each model. The pro-posed model takes advantage of CNN to extract features and Bi-LSTM to capture long term contextual information from past and future contexts. The proposed model is compared with CNN, Bi-LSTM, RNN, and CNN-LSTM models with pre-trained word embedding on six article datasets in Myanmar language."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Text classification"}, {"interim": "Myanmar Language"}, {"interim": "deep learning"}, {"interim": "Pre-trained word embedding"}, {"interim": "CNN"}, {"interim": "RNN"}, {"interim": "CNN-RNN"}, {"interim": "CNN-LSTM"}, {"interim": "Bi-LSTM"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_no", "date": [{"dateType": "Available", "dateValue": "2020-12-15"}], "displaytype": "preview", "filename": "Comparative Analysis of Deep Learning Models for Myanmar Text Classification.pdf", "filesize": [{"value": "427 KB"}], "format": "application/pdf", "licensefree": "© Springer Nature Switzerland AG 2020", "licensetype": "license_free", "url": {"url": "https://meral.edu.mm/record/6851/files/Comparative Analysis of Deep Learning Models for Myanmar Text Classification.pdf"}, "version_id": "2f5daa5a-47c4-4399-bb7b-d47923af755a"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ACIIDS", "subitem_c_date": "4 March, 2020", "subitem_conference_title": "Asian Conference on Intelligent Information and Database Systems", "subitem_place": "Phuket, Thailand", "subitem_website": "https://link.springer.com/chapter/10.1007%2F978-3-030-41964-6_7"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Myat Sapal Phyu"}, {"subitem_authors_fullname": "Khin Thandar Nwet"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2020-12-15"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "10.1007/978-3-030-41964-6_7"}, "item_title": "Comparative Analysis of Deep Learning Models for Myanmar Text Classification", "item_type_id": "21", "owner": "45", "path": ["1596102355557"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000006851", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-12-15"}, "publish_date": "2020-12-15", "publish_status": "0", "recid": "6851", "relation": {}, "relation_version_is_last": true, "title": ["Comparative Analysis of Deep Learning Models for Myanmar Text Classification"], "weko_shared_id": -1}