{"_buckets": {"deposit": "ce282c10-9ca3-4674-87e6-21395f9154a6"}, "_deposit": {"created_by": 73, "id": "7975", "owner": "73", "owners": [73], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "7975"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00007975", "sets": ["user-miit"]}, "communities": ["miit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Implementation of Spelling Error Words Correcting System Using Bi-gram Model and Approximate String Matching Algorithm", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Natural language processing is a subfield of AI. Natural languages are human languages such as English and Chinese, etc. NLP enables computer systems to understand written or spoken utterances made in human languages. There are many steps in NLP. This system uses morphological analysis steps for spelling checking. The system uses bi-gram model to reduce search space and then approximate string matching is used to suggest correct word. This system will assume the words in the sentences, containing in the lexicon or dictionary. Even the word is correct, however, if the word is not containing in the dictionary, the known word part of speech tagging process will determine the word as an misspelled word. Thus, the system assumes the words of the input sentences are containing in the dictionary. The system also provides to update dictionary to add new vocabulary."}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2021-02-04"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Implementation of Spelling Error Words Correcting System.pdf", "filesize": [{"value": "490 KB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_3", "mimetype": "application/pdf", "size": 490000.0, "url": {"url": "https://meral.edu.mm/record/7975/files/Implementation of Spelling Error Words Correcting System.pdf"}, "version_id": "f05cb3e5-5553-4549-9f51-abda839665b8"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_journal_title": "Proceedings of the Third Conference on Applied Information and Communication Technology", "subitem_pages": "Pages 193"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Phyo Hay Mar Wai"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Journal article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2010-12-03"}, "item_title": "Implementation of Spelling Error Words Correcting System Using Bi-gram Model and Approximate String Matching Algorithm", "item_type_id": "21", "owner": "73", "path": ["1582963674932", "1597396989070"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000007975", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2021-02-04"}, "publish_date": "2021-02-04", "publish_status": "0", "recid": "7975", "relation": {}, "relation_version_is_last": true, "title": ["Implementation of Spelling Error Words Correcting System Using Bi-gram Model and Approximate String Matching Algorithm"], "weko_shared_id": -1}