Perkembangan Part-of-Speech Tagger Bahasa Indonesia

Kamayani, Mia

doi:10.26418/jlk.v2i2.20

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…This is actually a limitation for Indonesian language as there is no general corpus with POS tagger large enough for this task. The attempt to create a tagged Indonesian corpus is already done by Fu et al, (2018), Dinakaramani et al, (2014), Christanti et al, (2016) and Kamayani, (2019) with a similar recommendation to expand the available corpus.…”

Section: Introductionmentioning

confidence: 99%

Corpus-Based Terms Extraction in Linguistics Domain for Indonesian Language

2022

kata

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This research aims to extract the mono-lexical and poly-lexical terms from linguistics domain in Indonesian language. As the terminology and lexicology concept is somehow blurry, this research applies CTT by Cabré to do the terms extraction procedure. The corpus-based terminology method is applied in this research to get the best mono-lexical and poly-lexical terms possible. To compile the general and the specialized corpus in this research, AntConc is applied as an instrument. Even though the result is noisy, further analysis about the term limitation manually makes this research semi-automatic. The result shows that the limitation in language and words structure helps this research to delimit the mono-lexical terms extracted in this research. Furthermore, the mono-lexical terms extracted act as the starting point for poly-lexical terms.

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Section: Introductionmentioning

confidence: 99%

Corpus-Based Terms Extraction in Linguistics Domain for Indonesian Language

2022

kata

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“…This study will implement Part-of-Speech tagger (POS tagger) method in NLP to extract parts of speech of each word, such as noun, verb, adjective, etc. Some POS tagger in Indonesian language has been conducted in a rulebased model, a probabilistic model, hidden markov model and neural network model [13] such as INA-NLP POS tagger [14], POS tag Indonesia [15], deep neural network POS tagger [16], etc. Some research used POS tagger in Indonesian language directly such as document subjectivity and target detection in opinion mining [17] to extract public opinions [18]; modified opinion mining rules to extract multilabel students complaints [19]; and also Indonesian POS tagging system for computer-aided independent language learning [20].…”

Section: Introductionmentioning

confidence: 99%

Smart Dissemination by Using Natural Language Processing Technology

Fahrudin

Kastaman

Meideni

et al. 2020

JISEBI

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Background: Recently, WhatsApp has become the world's most popular text and voice messaging application with 1.5 billion users. A lot of WhatsApp Application Programming Interface (API) has also been established to be connected to other applications. On the other hand, the development of natural language processing (NLP) for WhatsApp messages has snowballed. There are extensive studies on the dissemination information using WhatsApp but the study on NLP involving data from WhatsApp is lacking.Objective: This study aims to implement NLP in smart dissemination applications by using WhatsApp API.Methods: We build a framework that embeds an intelligent system based on the NLP in WhatsApp API to disseminate a dynamic message. Some of the sentences are used to evaluate the accuracy of this application.Results: Smart dissemination consists of dynamic filter and dynamic content. Dynamic filter was conducted by using the POS tagger and clause statement. Meanwhile, dynamic content was built by using the replace MySQL function. There are twofold limitation: the application could not transform a message that matches rule <3> with conjunction “dan”; has the same attribute before and after <CC> tag; and the maximum of the logical operator is one type for coordinating conjunction (AND/OR) in one sentence.Conclusion: Our framework can be used for dynamic dissemination of messages using dynamic message content and dynamic message recipient with an accuracy of 95% from twenty sample messages.

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“…Penelitian tentang POS tagging pada beberapa bahasa di Indonesia telah banyak dilakukan dengan berbagai metode. Penelitian yang membandingkan berbagai metode dan corpus dari tahun 2008 sampai 2019, menghasilkan metode terbaik yaitu arsitektur neural network dengan menggunakan bidirectional LSTM dan CRF dengan nilai akurasi sebesar 95,68% (KAMAYANI, 2019). Penelitian tentang POS Tagging berbasis aturan dan distribusi probabilitas maximum entropy pada bahasa Jawa Krama mendapatkan hasil akurasi 97.67% (PRAMUDITA, et al, 2016).…”

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Klasifikasi Kelas Kata (Part-Of-Speech Tagging) untuk Bahasa Madura Menggunakan Algoritme Viterbi

Firmansyah

Adikara

Adinugroho

2021

JTIIK

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Bahasa manusia adalah bahasa yang digunakan oleh manusia dalam bentuk tulisan maupun suara. Banyak teknologi/aplikasi yang mengolah bahasa manusia, bidang tersebut bernama Natural Language Processing yang merupakan ilmu yang mempelajari untuk mengolah dan mengekstraksi bahasa manusia pada perkembangan teknologi. Salah satu proses pada Natural Language Processing adalah Part-Of-Speech Tagging. Part-Of-Speech Tagging adalah klasifikasi kelas kata pada sebuah kalimat secara otomatis oleh teknologi, proses ini salah satunya berfungsi untuk mengetahui kata-kata yang memiliki lebih dari satu makna/arti (ambiguitas). Part-Of-Speech Tagging merupakan dasar dari Natural Language Processing lainnya, seperti penerjemahan mesin (machine translation), penghilangan ambiguitas makna kata (word sense disambiguation), dan analisis sentimen. Part-Of-Speech Tagging dilakukan pada bahasa manusia, salah satunya adalah bahasa Madura. Bahasa Madura adalah bahasa daerah yang digunakan oleh suku Madura dan memiliki morfologi yang mirip dengan bahasa Indonesia. Penelitian pada Part-Of-Speech Tagging pada bahasa Madura ini menggunakan algoritme Viterbi, terdapat 3 proses untuk implementasi algoritme Viterbi pada pada Part-Of-Speech Tagging bahasa Madura, yaitu pre-processing pada data training dan testing, perhitungan data latih dengan Hidden Markov Model dan klasifikasi kelas kata menggunakan algoritme Viterbi. Kelas kata (tagset) yang digunakan untuk klasifikasi kata pada bahasa Madura sebanyak 19 kelas, kelas kata tersebut dirancang oleh pakar. Pengujian sistem pada penelitian ini menggunakan perhitungan Multiclass Confusion Matrix. Hasil pengujian sistem mendapatkan nilai micro average accuracy sebesar 0,96 dan nilai micro average precision dan recall yang sama sebesar 0,68. Precision dan recall masih dapat ditingkatkan dengan menambahkan data yang lebih banyak lagi untuk pelatihan. AbstractNatural language is a form of language used by human, either in writing or speaking form. There is a specific field in computer science that processes natural language, which is called Natural Language Processing. It is a study of how to process and extract natural language on technology development. Part-Of-Speech Tagging is a method to assign a predefined set of tags (word classes) into a word or a phrase. This process is useful to understand the true meaning of a word with ambiguous meaning, which may have different meanings depending on the context. Part-Of-Speech Tagging is the basis of the other Natural Language Processing methods, such as machine translation, word sense disambiguation, and sentiment analysis. Part-Of-Speech Tagging used in natural languages, such as Madurese language. Madurese language is a local language used by Madurese and has a similar morphology as Indonesian language. Part-Of-Speech Tagging research on Madurese language using Viterbi algorithm, consists of 3 processes, which are training and testing corpus pre-processing, training the corpus by Hidden Markov Model, and tag classification using Viterbi algorithm. The number of tags used for words classification (tagsets) on Madurese language are 19 class, those tags were designed by an expert. Performance assessment was conducted using Multiclass Confusion Matrix calculation. The system achieved a micro average accuracy score of 0,96, and micro average precision score is equal to recall of 0,68. Precision and recall can still be improved by adding more data for training.

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Perkembangan Part-of-Speech Tagger Bahasa Indonesia

Cited by 6 publications

References 12 publications

Corpus-Based Terms Extraction in Linguistics Domain for Indonesian Language

Corpus-Based Terms Extraction in Linguistics Domain for Indonesian Language

Smart Dissemination by Using Natural Language Processing Technology

Klasifikasi Kelas Kata (Part-Of-Speech Tagging) untuk Bahasa Madura Menggunakan Algoritme Viterbi

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