Log in
Language:

MERAL Myanmar Education Research and Learning Portal

  • Top
  • Universities
  • Ranking
To
lat lon distance
To

Field does not validate



Index Link

Index Tree

Please input email address.

WEKO

One fine body…

WEKO

One fine body…

Item

{"_buckets": {"deposit": "9e55f6ad-e6e4-4674-a5fa-8ca585f56dfc"}, "_deposit": {"created_by": 45, "id": "6744", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "6744"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00006744", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Comparative Study of Naïve Bayesian Classifier and Transformation-Based Learning for Myanmar Function Tagging", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "This paper describes the use of two machine\nlearning techniques, Naive Bayesian classifier (NB) and\ntransformation-based learning (TBL), to address the task of\nassigning function tags to Myanmar sentences. Function\ntagging is a process of assigning syntactic categories like\nsubject, object, time and location to each word in the text\ndocument. It is an important step in Natural Language\nProcessing. Function tags can help to improve the\nperformance of Myanmar to English machine translation\nsystem. In this paper, we present a comparison of two methods\nin our experiments. The results showed that TBL was better\nand outperformed NB and there was a slight difference\nbetween the results."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Naïve Bayesian"}, {"interim": "transformation-based learning"}, {"interim": "function tagging"}, {"interim": "Myanmar sentences"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-12-11"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "9. Comparative Study of Naive Bayesian Classifier and Transformation-Based Learning for Myanmar Function Tagging.pdf", "filesize": [{"value": "555 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "application/pdf", "size": 555000.0, "url": {"url": "https://meral.edu.mm/api/files/9e55f6ad-e6e4-4674-a5fa-8ca585f56dfc/9.%20Comparative%20Study%20of%20Naive%20Bayesian%20Classifier%20and%20Transformation-Based%20Learning%20for%20Myanmar%20Function%20Tagging.pdf"}, "version_id": "ca9a259c-1997-4586-a211-13070532f064"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICCSIT\u00272012", "subitem_c_date": "28-29 April, 2012", "subitem_conference_title": "2nd International Conference on Computer Science and Information Technology", "subitem_place": "Singapore"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Win Win Thant"}, {"subitem_authors_fullname": "Tin Myat Htwe"}, {"subitem_authors_fullname": "Ni Lar Thein"}]}]}, "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": "2012-04-29"}, "item_title": "Comparative Study of Naïve Bayesian Classifier and Transformation-Based Learning for Myanmar Function Tagging", "item_type_id": "21", "owner": "45", "path": ["1596102355557"], "permalink_uri": "https://meral.edu.mm/records/6744", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-12-11"}, "publish_date": "2020-12-11", "publish_status": "0", "recid": "6744", "relation": {}, "relation_version_is_last": true, "title": ["Comparative Study of Naïve Bayesian Classifier and Transformation-Based Learning for Myanmar Function Tagging"], "weko_shared_id": -1}
  1. University of Information Technology
  2. Faculty of Computer Science

Comparative Study of Naïve Bayesian Classifier and Transformation-Based Learning for Myanmar Function Tagging

https://meral.edu.mm/records/6744
https://meral.edu.mm/records/6744
7adc3a63-900b-4409-8976-ad3220bd2867
9e55f6ad-e6e4-4674-a5fa-8ca585f56dfc
Name / File License Actions
9. 9. Comparative Study of Naive Bayesian Classifier and Transformation-Based Learning for Myanmar Function Tagging.pdf (555 Kb)
Publication type
Conference paper
Upload type
Publication
Title
Title Comparative Study of Naïve Bayesian Classifier and Transformation-Based Learning for Myanmar Function Tagging
Language en
Publication date 2012-04-29
Authors
Win Win Thant
Tin Myat Htwe
Ni Lar Thein
Description
This paper describes the use of two machine
learning techniques, Naive Bayesian classifier (NB) and
transformation-based learning (TBL), to address the task of
assigning function tags to Myanmar sentences. Function
tagging is a process of assigning syntactic categories like
subject, object, time and location to each word in the text
document. It is an important step in Natural Language
Processing. Function tags can help to improve the
performance of Myanmar to English machine translation
system. In this paper, we present a comparison of two methods
in our experiments. The results showed that TBL was better
and outperformed NB and there was a slight difference
between the results.
Keywords
Naïve Bayesian, transformation-based learning, function tagging, Myanmar sentences
Conference papers
ICCSIT'2012
28-29 April, 2012
2nd International Conference on Computer Science and Information Technology
Singapore
Back
0
0
views
downloads
See details
Views Downloads

Versions

Ver.1 2020-12-11 15:42:32.767862
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Export

OAI-PMH
  • OAI-PMH DublinCore
Other Formats
  • JSON

Confirm


Back to MERAL


Back to MERAL