PARALELISASI MAXIMUM ENTROPY PART OF SPEECH TAGGING UNTUK BAHASA INDONESIA DENGAN MAPREDUCE
Researches in natural languange processing indicated that more data led to better accuracy. Processing this large scale of data using single machine has its own limitation that can be handled by processing data in parallel. This research used MapReduce on part-of-speech (POS) tagging. MapReduce...
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| Format: | Thesis |
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[Yogyakarta] : Universitas Gadjah Mada
2011
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| _version_ | 1826043949063602176 |
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| author | Nurwidyantoro, Arif Winarko, Edi |
| author_facet | Nurwidyantoro, Arif Winarko, Edi |
| author_sort | Nurwidyantoro, Arif |
| collection | UGM |
| description | Researches in natural languange processing indicated that more data led to
better accuracy. Processing this large scale of data using single machine has its
own limitation that can be handled by processing data in parallel.
This research used MapReduce on part-of-speech (POS) tagging.
MapReduce is programming model developed for processing large data, while
POS tagging is one the earliest steps in natural language processing. POS tagging
approach used in this research is Maximum Entropy model in Bahasa Indonesia.
MapReduce model is implemented in some parts of training and tagging process.
MapReduce is implemented in dictionary, tagtoken, and feature creation,
and also in calculation using improved iterative scaling (IIS). It is found out that
calculation using IIS could not implemented using MapReduce model, because
there is updating probability parameters that closely related so that it could not
implemented in parallel. The experiments conducted using 100,000 and 1,000,000
words training corpus from Pan Localization and 12,000 words training corpus
used in Wicaksono and Purwarianti's research. The experiments showed that total
training time using MapReduce is faster than without using it. However,
MapReduce's result reading time inside training process slow down the training
total time.
Tagging experiments conducted using different numbers of map and reduce
process on different sizes corpora gathered from various news sites. The
experiments showed MapReduce implementation could speedup the tagging
process. The fastest result is shown by tagging process using 1,000,000 words
corpus and 30 map process. |
| first_indexed | 2024-03-13T22:11:49Z |
| format | Thesis |
| id | oai:generic.eprints.org:90876 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-13T22:11:49Z |
| publishDate | 2011 |
| publisher | [Yogyakarta] : Universitas Gadjah Mada |
| record_format | dspace |
| spelling | oai:generic.eprints.org:908762020-02-20T08:56:23Z https://repository.ugm.ac.id/90876/ PARALELISASI MAXIMUM ENTROPY PART OF SPEECH TAGGING UNTUK BAHASA INDONESIA DENGAN MAPREDUCE Nurwidyantoro, Arif Winarko, Edi Electrical and Electronic Engineering Researches in natural languange processing indicated that more data led to better accuracy. Processing this large scale of data using single machine has its own limitation that can be handled by processing data in parallel. This research used MapReduce on part-of-speech (POS) tagging. MapReduce is programming model developed for processing large data, while POS tagging is one the earliest steps in natural language processing. POS tagging approach used in this research is Maximum Entropy model in Bahasa Indonesia. MapReduce model is implemented in some parts of training and tagging process. MapReduce is implemented in dictionary, tagtoken, and feature creation, and also in calculation using improved iterative scaling (IIS). It is found out that calculation using IIS could not implemented using MapReduce model, because there is updating probability parameters that closely related so that it could not implemented in parallel. The experiments conducted using 100,000 and 1,000,000 words training corpus from Pan Localization and 12,000 words training corpus used in Wicaksono and Purwarianti's research. The experiments showed that total training time using MapReduce is faster than without using it. However, MapReduce's result reading time inside training process slow down the training total time. Tagging experiments conducted using different numbers of map and reduce process on different sizes corpora gathered from various news sites. The experiments showed MapReduce implementation could speedup the tagging process. The fastest result is shown by tagging process using 1,000,000 words corpus and 30 map process. [Yogyakarta] : Universitas Gadjah Mada 2011 Thesis NonPeerReviewed Nurwidyantoro, Arif and Winarko, Edi (2011) PARALELISASI MAXIMUM ENTROPY PART OF SPEECH TAGGING UNTUK BAHASA INDONESIA DENGAN MAPREDUCE. Bachelor thesis, Universitas Gadjah Mada. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=53205 |
| spellingShingle | Electrical and Electronic Engineering Nurwidyantoro, Arif Winarko, Edi PARALELISASI MAXIMUM ENTROPY PART OF SPEECH TAGGING UNTUK BAHASA INDONESIA DENGAN MAPREDUCE |
| title | PARALELISASI MAXIMUM ENTROPY PART OF SPEECH TAGGING
UNTUK BAHASA INDONESIA DENGAN MAPREDUCE |
| title_full | PARALELISASI MAXIMUM ENTROPY PART OF SPEECH TAGGING
UNTUK BAHASA INDONESIA DENGAN MAPREDUCE |
| title_fullStr | PARALELISASI MAXIMUM ENTROPY PART OF SPEECH TAGGING
UNTUK BAHASA INDONESIA DENGAN MAPREDUCE |
| title_full_unstemmed | PARALELISASI MAXIMUM ENTROPY PART OF SPEECH TAGGING
UNTUK BAHASA INDONESIA DENGAN MAPREDUCE |
| title_short | PARALELISASI MAXIMUM ENTROPY PART OF SPEECH TAGGING
UNTUK BAHASA INDONESIA DENGAN MAPREDUCE |
| title_sort | paralelisasi maximum entropy part of speech tagging untuk bahasa indonesia dengan mapreduce |
| topic | Electrical and Electronic Engineering |
| work_keys_str_mv | AT nurwidyantoroarif paralelisasimaximumentropypartofspeechtagginguntukbahasaindonesiadenganmapreduce AT winarkoedi paralelisasimaximumentropypartofspeechtagginguntukbahasaindonesiadenganmapreduce |