Development of Part of Speech Tagger using Deep Learning

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.

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Improving Brill's tagger lexical and transformation rule for Afaan Oromo language

10.7287/peerj.preprints.1225v1 ◽

2015 ◽

Author(s):

Abraham G Ayana

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Transformation Rule ◽

Initial State ◽

Training Corpus ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

Speech Tagging

Natural Language Processing (NLP) refers to Human-like language processing which reveals that it is a discipline within the field of Artificial Intelligence (AI). However, the ultimate goal of research on Natural Language Processing is to parse and understand language, which is not fully achieved yet. For this reason, much research in NLP has focused on intermediate tasks that make sense of some of the structure inherent in language without requiring complete understanding. One such task is part-of-speech tagging, or simply tagging. Lack of standard part of speech tagger for Afaan Oromo will be the main obstacle for researchers in the area of machine translation, spell checkers, dictionary compilation and automatic sentence parsing and constructions. Even though several works have been done in POS tagging for Afaan Oromo, the performance of the tagger is not sufficiently improved yet. Hence,the aim of this thesis is to improve Brill’s tagger lexical and transformation rule for Afaan Oromo POS tagging with sufficiently large training corpus. Accordingly, Afaan Oromo literatures on grammar and morphology are reviewed to understand nature of the language and also to identify possible tagsets. As a result, 26 broad tagsets were identified and 17,473 words from around 1100 sentences containing 6750 distinct words were tagged for training and testing purpose. From which 258 sentences are taken from the previous work. Since there is only a few ready made standard corpuses, the manual tagging process to prepare corpus for this work was challenging and hence, it is recommended that a standard corpus is prepared. Transformation-based Error driven learning are adapted for Afaan Oromo part of speech tagging. Different experiments are conducted for the rule based approach taking 20% of the whole data for testing. A comparison with the previously adapted Brill’s Tagger made. The previously adapted Brill’s Tagger shows an accuracy of 80.08% whereas the improved Brill’s Tagger result shows an accuracy of 95.6% which has an improvement of 15.52%. Hence, it is found that the size of the training corpus, the rule generating system in the lexical rule learner, and moreover, using Afaan Oromo HMM tagger as initial state tagger have a significant effect on the improvement of the tagger.

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Part of Speech Tagging for Arabic Long Sentence

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.27.17671 ◽

2018 ◽

Vol 7 (3.27) ◽

pp. 125

Author(s):

Ahmed H. Aliwy ◽

Duaa A. Al_Raza

Keyword(s):

Language Processing ◽

Arabic Language ◽

Data Set ◽

English Sentence ◽

Suggested Approach ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

N Gram ◽

Speech Tagging

Part Of Speech (POS) tagging of Arabic words is a difficult and non-travail task it was studied in details for the last twenty years and its performance affects many applications and tasks in area of natural language processing (NLP). The sentence in Arabic language is very long compared with English sentence. This affect tagging process for any approach deals with complete sentence at once as in Hidden Markov Model HMM tagger. In this paper, new approach is suggested for using HMM and n-grams taggers for tagging Arabic words in a long sentence. The suggested approach is very simple and easy to implement. It is implemented on data set of 1000 documents of 526321 tokens annotated manually (containing punctuations). The results shows that the suggested approach has higher accuracy than HMM and n-gram taggers. The F-measures were 0.888, 0.925 and 0.957 for n-grams, HMM and the suggested approach respectively.

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Improving Brill's tagger lexical and transformation rule for Afaan Oromo language

10.7287/peerj.preprints.1225 ◽

2015 ◽

Author(s):

Abraham G Ayana

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Transformation Rule ◽

Initial State ◽

Training Corpus ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

Speech Tagging

Natural Language Processing (NLP) refers to Human-like language processing which reveals that it is a discipline within the field of Artificial Intelligence (AI). However, the ultimate goal of research on Natural Language Processing is to parse and understand language, which is not fully achieved yet. For this reason, much research in NLP has focused on intermediate tasks that make sense of some of the structure inherent in language without requiring complete understanding. One such task is part-of-speech tagging, or simply tagging. Lack of standard part of speech tagger for Afaan Oromo will be the main obstacle for researchers in the area of machine translation, spell checkers, dictionary compilation and automatic sentence parsing and constructions. Even though several works have been done in POS tagging for Afaan Oromo, the performance of the tagger is not sufficiently improved yet. Hence,the aim of this thesis is to improve Brill’s tagger lexical and transformation rule for Afaan Oromo POS tagging with sufficiently large training corpus. Accordingly, Afaan Oromo literatures on grammar and morphology are reviewed to understand nature of the language and also to identify possible tagsets. As a result, 26 broad tagsets were identified and 17,473 words from around 1100 sentences containing 6750 distinct words were tagged for training and testing purpose. From which 258 sentences are taken from the previous work. Since there is only a few ready made standard corpuses, the manual tagging process to prepare corpus for this work was challenging and hence, it is recommended that a standard corpus is prepared. Transformation-based Error driven learning are adapted for Afaan Oromo part of speech tagging. Different experiments are conducted for the rule based approach taking 20% of the whole data for testing. A comparison with the previously adapted Brill’s Tagger made. The previously adapted Brill’s Tagger shows an accuracy of 80.08% whereas the improved Brill’s Tagger result shows an accuracy of 95.6% which has an improvement of 15.52%. Hence, it is found that the size of the training corpus, the rule generating system in the lexical rule learner, and moreover, using Afaan Oromo HMM tagger as initial state tagger have a significant effect on the improvement of the tagger.

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A Statistical Method for Evaluating Performance of Part of Speech Tagger for Gujarati

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1492.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 3899-3903

Keyword(s):

Natural Language Processing ◽

Markov Model ◽

Language Processing ◽

Hidden Markov ◽

Model Error ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

Textual Content ◽

Speech Tagging

Part of Speech Tagging has continually been a difficult mission in the era of Natural Language Processing. This article offers POS tagging for Gujarati textual content the use of Hidden Markov Model. Using Gujarati text annotated corpus for training checking out statistics set are randomly separated. 80% accuracy is given by model. Error analysis in which the mismatches happened is likewise mentioned in element.

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Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text

10.31219/osf.io/gvj45 ◽

2019 ◽

Author(s):

Zied Baklouti

Keyword(s):

Mathematical Model ◽

Language Processing ◽

Hidden Markov ◽

Stochastic Methods ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

Unknown Words ◽

High Level ◽

Speech Tagging

Natural Language Processing (NLP) is a branch of machine learning that gives the machines the ability to decode human languages. Part-of-speech tagging (POS tagging) is a preprocessing task that requires an annotated corpora. Rule-based and stochastic methods showed great results for POS tag prediction. On this work, I performed a mathematical model based on Hidden Markov structures and I obtained a high level accuracy of ingredients extracted from text recipe which is a performance greater than what traditional methods could make without unknown words consideration.

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New Combined Method to Improve Arabic POS Tagging

Journal of Autonomous Intelligence ◽

10.32629/jai.v1i2.30 ◽

2019 ◽

Vol 1 (2) ◽

pp. 23

Author(s):

Mohamed Labidi

Keyword(s):

Language Processing ◽

Arabic Language ◽

Combined Method ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Knowledge Based ◽

Part Of Speech ◽

Language Characteristics ◽

Speech Tagging ◽

Statistical Rule

One of the important tasks in Natural language processing is the part of speech tagging. For the Arabic language we have a lot of works but their performances do not rise to the required level, due to the complexity of the task and the Arabic language characteristics. In this work we study a combination between twodifferent approaches for Arabic POS-Taggers. The first one isa maximum entropy-based one, and the second is a statistical/rule-based one. Furthermore, we add a knowledge-based method to annotate Arabic particles. Our idea improves the accuracy rate. We passed from almost 85% to almost 90% using our combined method, which seem promoter.

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Arabic Part-of-Speech Tagger, an Approach Based on Neural Network Modelling

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.29.14009 ◽

2018 ◽

Vol 7 (2.29) ◽

pp. 742

Author(s):

Rabab Ali Abumalloh ◽

Hasan Muaidi Al-Serhan ◽

Othman Bin Ibrahim ◽

Waheeb Abu-Ulbeh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

Artificial Neural ◽

Speech Tagging

POS-tagging gained the interest of researchers in computational linguistics sciences in the recent years. Part-of-speech tagging systems assign the proper grammatical tag or morpho-syntactical category labels automatically to every word in the corpus per its appearance on the text. POS-tagging serves as a fundamental and preliminary step in linguistic analysis which can help in developing many natural language processing applications such as: word processing systems, spell checking systems, building dictionaries and in parsing systems. Arabic language gained the interest of researchers which led to increasing demand for Arabic natural language processing systems. Artiﬁcial neural networks has been applied in many applications such as speech recognition and part of speech prediction, but it is considered as a new approach in Part-of-speech tagging. In this research, we developed an Arabic POS-tagger using artificial neural network. A corpus of 20,620 words, which were manually assigned to the appropriate tags was developed and used to train the artificial neural network and to test the part of speech tagger systems’ overall performance. The accuracy of the developed tagger reaches 89.04% using the testing dataset. While, it reaches 98.94% using the training dataset. By combining the two datasets, the accuracy rate for the whole system is 96.96%.

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Amazigh part-of-speech tagging with machine learning and deep learning

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v24.i3.pp1814-1822 ◽

2021 ◽

Vol 24 (3) ◽

pp. 1814

Author(s):

Otman Maarouf ◽

Rachid El Ayachi ◽

Mohamed Biniz

Keyword(s):

Decision Tree ◽

Language Processing ◽

Conditional Random Fields ◽

Short Term Memory ◽

Long Distance ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

French And English ◽

Speech Tagging

Natural language processing (NLP) is a part of artificial intelligence that dissects, comprehends, and changes common dialects with computers in composed and spoken settings. At that point in scripts. Grammatical features part-of-speech (POS) allow marking the word as per its statement. We find in the literature that POS is used in a few dialects, in particular: French and English. This paper investigates the attention-based long short-term memory (LSTM) networks and simple recurrent neural network (RNN) in Tifinagh POS tagging when it is compared to conditional random fields (CRF) and decision tree. The attractiveness of LSTM networks is their strength in modeling long-distance dependencies. The experiment results show that LSTM networks perform better than RNN, CRF and decision tree that has a near performance.

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Lexical Rule and Lexicon Effect for Part of Speech Tagging Bahasa Madura

Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer ◽

10.30812/matrik.v18i1.332 ◽

2018 ◽

Vol 18 (1) ◽

pp. 65-72

Author(s):

Nindian Puspa Dewi ◽

Ubaidi Ubaidi

Keyword(s):

Text Processing ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

Speech Tagging ◽

Bahasa Indonesia

POS Tagging adalah dasar untuk pengembangan Text Processing suatu bahasa. Dalam penelitian ini kita meneliti pengaruh penggunaan lexicon dan perubahan morfologi kata dalam penentuan tagset yang tepat untuk suatu kata. Aturan dengan pendekatan morfologi kata seperti awalan, akhiran, dan sisipan biasa disebut sebagai lexical rule. Penelitian ini menerapkan lexical rule hasil learner dengan menggunakan algoritma Brill Tagger. Bahasa Madura adalah bahasa daerah yang digunakan di Pulau Madura dan beberapa pulau lainnya di Jawa Timur. Objek penelitian ini menggunakan Bahasa Madura yang memiliki banyak sekali variasi afiksasi dibandingkan dengan Bahasa Indonesia. Pada penelitian ini, lexicon selain digunakan untuk pencarian kata dasar Bahasa Madura juga digunakan sebagai salah satu tahap pemberian POS Tagging. Hasil ujicoba dengan menggunakan lexicon mencapai akurasi yaitu 86.61% sedangkan jika tidak menggunakan lexicon hanya mencapai akurasi 28.95 %. Dari sini dapat disimpulkan bahwa ternyata lexicon sangat berpengaruh terhadap POS Tagging.

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