working progress and outline of project plan wbs 1

PRESENTED BY ELVIRA NURFADHILAH * B a s e d O n Wo r k B y M o h a m m a d Te d u h U l i n i a n s y a h , S h u n I s h i z a k i ,

A n d K i y o k o U c h i y a m a

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Name : Elvira Nurfadhilah Working at : Agency for the Assessment & Application of Technology (BPPT)

Laboratory : Intelligence Computing Laboratory (ICL) Specialisation field : Image Processing and Natural Language Processing Email : elvira.nurfadhilah@bppt.go.id/ elvira.nurfadhilah@gmail.com /

Educational background Under Graduate : Bogor Agriculture University in Computer Science (2011)

Graduate : Bogor Agriculture University in Computer Science (2015)

Joined at BPPT : 2014

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Intelligent Computing Laboratory (ICL) at the Center for Information and Communication Technology (PTIK), BPPT.

ICL deals with image processing, computer vision, language technology and signal processing.

Portal Bahasa (Stemmer and Concordance)

Statistical Machine Translation Text-To- Speech Etc.

Fingerprint and Latten Fingerprint Iris Face and Face sketch Blood vessel etc.

Developing a malaria diagnosis tool based on images of thin and thick smears.

Natural Language Processing

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o Background o Experimental Data o Method o Results and Discussion o Utilizing the proposed technique for Wordnet o Demo Program

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BACKGROUND

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Ambiguities arise when a single lexical word may have been created by more than one pos-sible combination of affixes.

Example: beruang:

o beruang (Noun Animal)

o ber ( uang ( Noun Concrete )) : Verb Intransitive

o be ( ruang ( Noun Abstract Concept )) :

Verb Intransitive

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EXPERIMENTAL DATA

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A corpus consists of articles (politics,economics,sports,etc.) downloaded from"Kompas"daily newspaper website (http://www.kompas.com). The corpus contains 20,579,771 words in 1,105,156 sentences.

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There are more than 800 combinations of affixes (prefixes, suffixes, and infixes)

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METHOD

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Linking all possible nodes Retrieve all possible POS tags

candidates from root word dictionary and affix table

Assign linking costs between nodes, search minimum cost,

and decide proper POS tags for each words

Process 1 Process 2 Process 3

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Beruang

(be + uang)

(ber + ruang)

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Example

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Let p1 be one POS and p2 another one, where p2 directly follows pi. The cost of the pair (p1,p2) is:

Cost (p1,p2) = 2log(N/n(p1,p2))

where n(p1,p2) is the number of (p1,p2) pairs which appear in the data. N is the total number of all of the pairs of POS tags in the data.

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Example :

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Cost (pronoun, adverb) = 4.9

Cost (pronoun, adjective) = 8.3

Cost (pronoun, conjunction) = 8.9

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An Example of Possible Analysis for a Simple Input Sentence

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RESULTS & DISCUSSION

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Reference • Uliniansyah MT, Ishizaki S, Uchiyama K. 2004. Solving Ambiguities in

Indonesian Words by Morphological Analysis Using Minimum Connectivity Cost. Journal of Natural Language Processing, Vol. 11, No. 1

• Kridalaksana, H.(1996). Pembentukan Kata dalam Bahasa Indonesia. PT Gramedia Pustaka Utama.

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Utilizing the proposed technique for WordNet We can use Wordnet and this technique to choose the proper sense of the word in a sentence.

Contoh :

Dia \pronoun sedang\adverb berada\verb intransitive di\partical location dalam\noun abstract location kamar\noun building

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Demo Program

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