A Rule-Based Approach for Marathi Part-of-Speech Tagging

2021 ◽  
pp. 773-785
Author(s):  
P. Kadam Vaishali ◽  
Khandale Kalpana ◽  
C. Namrata Mahender
Keyword(s):  
Rule Based ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Rule Based Approach ◽  
Speech Tagging

scholarly journals Part of Speech Tagging in Manipuri: A Rule based Approach

2012 ◽  
Vol 51 (14) ◽  
pp. 31-36
Author(s):  
Kh RajuSingha ◽  
Bipul Syam Purkayastha ◽  
Kh Dhiren Singha
Keyword(s):  
Rule Based ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Rule Based Approach ◽  
Speech Tagging

scholarly journals Pengaruh Part of Speech Tagging Berbasis Aturan dan Distribusi Probabilitas Maximum Entropy untuk Bahasa Jawa Krama

2016 ◽  
Vol 7 (4) ◽  
Author(s):  
Hafiz Ridha Pramudita ◽  
Ema Utami ◽  
Armadyah Amborowati
Keyword(s):  
Maximum Entropy ◽  
Syntactic Category ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Speech Tagging ◽  
Local Languages

Abstract. Javanese language is one of the local languages in Indonesia, which is used by most of the population of Indonesia. The language has complex grammar to embrace the values of decency that is determined by the use of words containing courtesy known as Raos Alus. Every word in the Javanese belongs to a certain part of speech like what happens to other languages. Part of Speech (POS) tagging is a process to set syntactic category in a word such as nouns, verbs, or adjectives to every word in the document or text. This study examined the POS Tagging with Maximum Entropy and Rule Based for Javanese Krama—Higher Javanese--by using the Open NLP library to measure the maximum entropy. The results obtained are Maximum Entropy and Rule Based can be used for POS Tagging on Javanese Krama with the highest accuracy of 97.67%.Keywords: POS Tagging, NLP, Maximum Entropy, Rule Based, Javanese Krama LanguageAbstrak. Bahasa Jawa merupakan salah satu bahasa daerah di Indonesia yang dipakai oleh sebagian besar penduduk Indonesia. Bahasa Jawa memiliki tata bahasa yang kompleks karena menganut nilai-nilai kesopanan yang ditentukan berdasarkan penggunaan dengan kata-kata yang mengandung raos alus (rasa sopan). Setiap kata dalam Bahasa Jawa memiliki jenis kata atau part of speech tertentu seperti halnya dengan bahasa-bahasa lain. POS tagging merupakah bagian penting dari cakupan bidang ilmu Natural Languange Processing (NLP). Penelitian ini menguji POS Tagging dengan Berbasis Aturan dan distribusi probabilitas Maximum Entropy pada Bahasa Jawa Krama menggunakan library OpenNLP untuk mengukur maximum entropy. Hasil yang diperoleh adalah Maximum Entropy dan Rule Based dapat digunakan untuk POSTagging pada Bahasa Jawa Krama dengan akurasi tertinggi 97,67%.Kata Kunci: POS Tagging, NLP, Maximum Entropy, Rule Based, Bahasa Jawa Krama

scholarly journals PENENTUAN KELAS KATA PADA PART OF SPEECH TAGGING KATA AMBIGU BAHASA INDONESIA

2018 ◽  
Vol 2 (3) ◽  
pp. 157
Author(s):  
Ahmad Subhan Yazid ◽  
Agung Fatwanto
Keyword(s):  
Language Processing ◽  
Word Class ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Ambiguous Words ◽  
Computer Science Faculty ◽  
Speech Tagging ◽  
Bahasa Indonesia

Indonesian hold a fundamental role in the communication. There is ambiguous problem in its machine learning implementation. In the Natural Language Processing study, Part of Speech (POS) tagging has a role in the decreasing this problem. This study use the Rule Based method to determine the best word class for ambiguous words in Indonesian. This research follows some stages: knowledge inventory, making algorithms, implementation, Testing, Analysis, and Conclusions. The first data used is Indonesian corpus that was developed by Language department of Computer science Faculty, Indonesia University. Then, data is processed and shown descriptively by following certain rules and specification. The result is a POS tagging algorithm included 71 rules in flowchart and descriptive sentence notation. Refer to testing result, the algorithm successfully provides 92 labeling of 100 tested words (92%). The results of the implementation are influenced by the availability of rules, word class tagsets and corpus data.

scholarly journals A Template-Based Approach for Tagging Non-Vocalized Arabic Nouns

2021 ◽  
Vol 3 (32) ◽  
pp. 05-35
Author(s):  
Hashem Alsharif ◽  
Keyword(s):  
Linear Part ◽  
Arabic Language ◽  
Arabic Text ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Pos Tagger ◽  
Log Linear ◽  
Speech Tagging

There exist no corpora of Arabic nouns. Furthermore, in any Arabic text, nouns can be found in different forms. In fact, by tagging nouns in an Arabic text, the beginning of each sentence can determine whether it starts with a noun or a verb. Part of Speech Tagging (POS) is the task of labeling each word in a sentence with its appropriate category, which is called a Tag (Noun, Verb and Article). In this thesis, we attempt to tag non-vocalized Arabic text. The proposed POS Tagger for Arabic Text is based on searching for each word of the text in our lists of Verbs and Articles. Nouns are found by eliminating Verbs and Articles. Our hypothesis states that, if the word in the text is not found in our lists, then it is a Noun. These comparisons will be made for each of the words in the text until all of them have been tagged. To apply our method, we have prepared a list of articles and verbs in the Arabic language with a total of 112 million verbs and articles combined, which are used in our comparisons to prove our hypothesis. To evaluate our proposed method, we used pre-tagged words from "The Quranic Arabic Corpus", making a total of 78,245 words, with our method, the Template-based tagging approach compared with (AraMorph) a rule-based tagging approach and the Stanford Log-linear Part-Of-Speech Tagger. Finally, AraMorph produced 40% correctly-tagged words and Stanford Log-linear Part-Of-Speech Tagger produced 68% correctly-tagged words, while our method produced 68,501 correctly-tagged words (88%).

POS Tagging Bahasa Indonesia Dengan HMM dan Rule Based

2013 ◽  
Vol 8 (2) ◽  
Author(s):  
Kathryn Widhiyanti ◽  
Agus Harjoko
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Word Class ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Class Labelling ◽  
Speech Tagging

The research conduct a Part of Speech Tagging (POS-tagging) for text in Indonesian language, supporting another process in digitising natural language e.g. Indonesian language text parsing. POS-tagging is an automated process of labelling word classes for certain word in sentences (Jurafsky and Martin, 2000). The escalated issue is how to acquire an accurate word class labelling in sentence domain. The author would like to propose a method which combine Hidden Markov Model and Rule Based method. The expected outcome in this research is a better accurary in word class labelling, resulted by only using Hidden Markov Model. The labelling results –from Hidden Markov Model– are  refined by validating with certain rule, composed by the used corpus automatically. From the conducted research through some POST document, using Hidden Markov Model, produced 100% as the highest accurary for identical text within corpus. For different text within the referenced corpus, used words subjected in corpus, produced 92,2% for the highest accurary.

Fuzzy network model for part-of-speech tagging under small training data

1996 ◽  
Vol 2 (2) ◽  
pp. 95-110 ◽  
Author(s):  
JAE-HOON KIM ◽  
GIL CHANG KIM
Keyword(s):  
Network Model ◽  
Hidden Markov ◽  
Training Data ◽  
Rule Based ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Network Approaches ◽  
Fuzzy Network ◽  
Speech Tagging ◽  
Better Than

Recently, most part-of-speech tagging approaches, such as rule-based, probabilistic and neural network approaches, have shown very promising results. In this paper, we are particularly interested in probabilistic approaches, which usually require lots of training data to get reliable probabilities. We alleviate such a restriction of probabilistic approaches by introducing a fuzzy network model to provide a method for estimating more reliable parameters of a model under a small amount of training data. Experiments with the Brown corpus show that the performance of the fuzzy network model is much better than that of the hidden Markov model under a limited amount of training data.

scholarly journals Samawa Part of Speech Tagging using Brill Tagger

2019 ◽  
Vol 3 (2) ◽  
pp. 76-85
Author(s):  
Trienani Hariyanti ◽  
Saori Aida ◽  
Hiroyuki Kameda
Keyword(s):  
Native Speakers ◽  
Limited Data ◽  
Language Resources ◽  
Part Of Speech Tagging ◽  
Endangered Language ◽  
Information Tools ◽  
Part Of Speech ◽  
Lack Of Information ◽  
Rule Based Approach ◽  
Speech Tagging

There exist 7,097 living languages in the world cited by Ethnologue. Most of them, however, do not exist on the Internet as the objects of research. It indicates the gap in language resources. One of them is Samawa language which has over 500,000 native speakers and is identified as endangered language by UNESCO. What we known about Samawa so far is a lack of information, tools, and resources to maintain its sustainability. This paper aims to contribute to NLP, a growing field of research, by exploring Samawa part of speech tagging problem using rule-based approach, i.e. Brill tagger. It has been trained on very limited data of Samawa corpus, which is 24,627 tokens including punctuation marks with 24 tags of our original tagset. K-fold cross-validation (k = 5 and k = 10) was applied to compare Brill’s performance with Unigram, HMM, and TnT. Brill tagger with the combination of default tagger, Unigram, Bigram and Trigram as baseline tagger achieve higher accuracy over 95% than others. It suggests that the Brill tagger can be used to extend Samawa corpus automatically.

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