scholarly journals Improving accuracy of Part-of-Speech (POS) tagging using hidden markov model and morphological analysis for Myanmar Language

2020 ◽  
Vol 10 (2) ◽  
pp. 2023
Author(s):  
Dim Lam Cing ◽  
Khin Mar Soe
Keyword(s):  
Language Processing ◽  
High Performance ◽  
Markov Models ◽  
Hidden Markov ◽  
Morphological Structure ◽  
Word Segmentation ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Improving Accuracy ◽  
Pos Tagger

In Natural Language Processing (NLP), Word segmentation and Part-of-Speech (POS) tagging are fundamental tasks. The POS information is also necessary in NLP’s preprocessing work applications such as machine translation (MT), information retrieval (IR), etc. Currently, there are many research efforts in word segmentation and POS tagging developed separately with different methods to get high performance and accuracy. For Myanmar Language, there are also separate word segmentors and POS taggers based on statistical approaches such as Neural Network (NN) and Hidden Markov Models (HMMs). But, as the Myanmar language's complex morphological structure, the OOV problem still exists. To keep away from error and improve segmentation by utilizing POS data, segmentation and labeling should be possible at the same time.The main goal of developing POS tagger for any Language is to improve accuracy of tagging and remove ambiguity in sentences due to language structure. This paper focuses on developing word segmentation and Part-of- Speech (POS) Tagger for Myanmar Language. This paper presented the comparison of separate word segmentation and POS tagging with joint word segmentation and POS tagging.

scholarly journals A Hidden Markov Model-based Part of Speech Tagger for Shekki’noono Language

2021 ◽  
pp. 587-595
Author(s):  
Alebachew Chiche ◽  
Hiwot Kadi ◽  
Tibebu Bekele
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Language Processing ◽  
Hidden Markov ◽  
Parts Of Speech ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Pos Tagger

Natural language processing plays a great role in providing an interface for human-computer communication. It enables people to talk with the computer in their formal language rather than machine language. This study aims at presenting a Part of speech tagger that can assign word class to words in a given paragraph sentence. Some of the researchers developed parts of speech taggers for different languages such as English Amharic, Afan Oromo, Tigrigna, etc. On the other hand, many other languages do not have POS taggers like Shekki’noono language.  POS tagger is incorporated in most natural language processing tools like machine translation, information extraction as a basic component. So, it is compulsory to develop a part of speech tagger for languages then it is possible to work with an advanced natural language application. Because those applications enhance machine to machine, machine to human, and human to human communications. Although, one language POS tagger cannot be directly applied for other languages POS tagger. With the purpose for developing the Shekki’noono POS tagger, we have used the stochastic Hidden Markov Model. For the study, we have used 1500 sentences collected from different sources such as newspapers (which includes social, economic, and political aspects), modules, textbooks, Radio Programs, and bulletins.  The collected sentences are labeled by language experts with their appropriate parts of speech for each word.  With the experiments carried out, the part of speech tagger is trained on the training sets using Hidden Markov model. As experiments showed, HMM based POS tagging has achieved 92.77 % accuracy for Shekki’noono. And the POS tagger model is compared with the previous experiments in related works using HMM. As a future work, the proposed approaches can be utilized to perform an evaluation on a larger corpus.

scholarly journals Part-of-Speech Tagging via Deep Neural Networks for Northern-Ethiopic Languages

2020 ◽  
Vol 49 (4) ◽  
pp. 482-494
Author(s):  
Jurgita Kapočiūtė-Dzikienė ◽  
Senait Gebremichael Tesfagergish
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Parameter Tuning ◽  
Feed Forward Neural Network ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Pos Tagger

Deep Neural Networks (DNNs) have proven to be especially successful in the area of Natural Language Processing (NLP) and Part-Of-Speech (POS) tagging—which is the process of mapping words to their corresponding POS labels depending on the context. Despite recent development of language technologies, low-resourced languages (such as an East African Tigrinya language), have received too little attention. We investigate the effectiveness of Deep Learning (DL) solutions for the low-resourced Tigrinya language of the Northern-Ethiopic branch. We have selected Tigrinya as the testbed example and have tested state-of-the-art DL approaches seeking to build the most accurate POS tagger. We have evaluated DNN classifiers (Feed Forward Neural Network – FFNN, Long Short-Term Memory method – LSTM, Bidirectional LSTM, and Convolutional Neural Network – CNN) on a top of neural word2vec word embeddings with a small training corpus known as Nagaoka Tigrinya Corpus. To determine the best DNN classifier type, its architecture and hyper-parameter set both manual and automatic hyper-parameter tuning has been performed. BiLSTM method was proved to be the most suitable for our solving task: it achieved the highest accuracy equal to 92% that is 65% above the random baseline.

Part-of-speech tagging of Modern Hebrew text

2008 ◽  
Vol 14 (2) ◽  
pp. 223-251 ◽  
Author(s):  
ROY BAR-HAIM ◽  
KHALIL SIMA'AN ◽  
YOAD WINTER
Keyword(s):  
Markov Models ◽  
Full Generality ◽  
Semitic Languages ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Word Level ◽  
Modern Hebrew ◽  
Architectural Decision ◽  
Pos Tagger

AbstractWords in Semitic texts often consist of a concatenation ofword segments, each corresponding to a part-of-speech (POS) category. Semitic words may be ambiguous with regard to their segmentation as well as to the POS tags assigned to each segment. When designing POS taggers for Semitic languages, a major architectural decision concerns the choice of the atomic input tokens (terminal symbols). If the tokenization is at the word level, the output tags must be complex, and represent both the segmentation of the word and the POS tag assigned to each word segment. If the tokenization is at the segment level, the input itself must encode the different alternative segmentations of the words, while the output consists of standard POS tags. Comparing these two alternatives is not trivial, as the choice between them may have global effects on the grammatical model. Moreover, intermediate levels of tokenization between these two extremes are conceivable, and, as we aim to show, beneficial. To the best of our knowledge, the problem of tokenization for POS tagging of Semitic languages has not been addressed before in full generality. In this paper, we study this problem for the purpose of POS tagging of Modern Hebrew texts. After extensive error analysis of the two simple tokenization models, we propose a novel, linguistically motivated, intermediate tokenization model that gives better performance for Hebrew over the two initial architectures. Our study is based on the well-known hidden Markov models (HMMs). We start out from a manually devised morphological analyzer and a very small annotated corpus, and describe how to adapt an HMM-based POS tagger for both tokenization architectures. We present an effective technique for smoothing the lexical probabilities using an untagged corpus, and a novel transformation for casting the segment-level tagger in terms of a standard, word-level HMM implementation. The results obtained using our model are on par with the best published results on Modern Standard Arabic, despite the much smaller annotated corpus available for Modern Hebrew.

Part-of-Speech (POS) Tagging Using Deep Learning-Based Approaches on the Designed Khasi POS Corpus

2022 ◽  
Vol 21 (3) ◽  
pp. 1-24
Author(s):  
Sunita Warjri ◽  
Partha Pakray ◽  
Saralin A. Lyngdoh ◽  
Arnab Kumar Maji
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Research Work ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Corpus Size ◽  
Increase In Accuracy ◽  
Pos Tagger

Part-of-speech (POS) tagging is one of the research challenging fields in natural language processing (NLP). It requires good knowledge of a particular language with large amounts of data or corpora for feature engineering, which can lead to achieving a good performance of the tagger. Our main contribution in this research work is the designed Khasi POS corpus. Till date, there has been no form of any kind of Khasi corpus developed or formally developed. In the present designed Khasi POS corpus, each word is tagged manually using the designed tagset. Methods of deep learning have been used to experiment with our designed Khasi POS corpus. The POS tagger based on BiLSTM, combinations of BiLSTM with CRF, and character-based embedding with BiLSTM are presented. The main challenges of understanding and handling Natural Language toward Computational linguistics to encounter are anticipated. In the presently designed corpus, we have tried to solve the problems of ambiguities of words concerning their context usage, and also the orthography problems that arise in the designed POS corpus. The designed Khasi corpus size is around 96,100 tokens and consists of 6,616 distinct words. Initially, while running the first few sets of data of around 41,000 tokens in our experiment the taggers are found to yield considerably accurate results. When the Khasi corpus size has been increased to 96,100 tokens, we see an increase in accuracy rate and the analyses are more pertinent. As results, accuracy of 96.81% is achieved for the BiLSTM method, 96.98% for BiLSTM with CRF technique, and 95.86% for character-based with LSTM. Concerning substantial research from the NLP perspectives for Khasi, we also present some of the recently existing POS taggers and other NLP works on the Khasi language for comparative purposes.

Development of Part of Speech Tagger for Assamese Using HMM

2020 ◽  
pp. 1139-1148
Author(s):  
Surjya Kanta Daimary ◽  
Vishal Goyal ◽  
Madhumita Barbora ◽  
Umrinderpal Singh
Keyword(s):  
Language Processing ◽  
Stochastic Models ◽  
Hidden Markov ◽  
Stochastic Approach ◽  
Point Of View ◽  
Part Of Speech ◽  
Pos Tagger ◽  
Asian Languages ◽  
Free Word ◽  
Assamese Language

This article presents the work on the Part-of-Speech Tagger for Assamese based on Hidden Markov Model (HMM). Over the years, a lot of language processing tasks have been done for Western and South-Asian languages. However, very little work is done for Assamese language. So, with this point of view, the POS Tagger for Assamese using Stochastic Approach is being developed. Assamese is a free word-order, highly agglutinate and morphological rich language, thus developing POS Tagger with good accuracy will help in development of other NLP task for Assamese. For this work, an annotated corpus of 271,890 words with a BIS tagset consisting of 38 tag labels is used. The model is trained on 256,690 words and the remaining words are used in testing. The system obtained an accuracy of 89.21% and it is being compared with other existing stochastic models.

scholarly journals MulTed: a multilingual aligned and tagged parallel corpus

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imad Zeroual ◽  
Abdelhak Lakhouaja
Keyword(s):  
Language Processing ◽  
Corpus Linguistics ◽  
Statistical Machine Translation ◽  
Language Recognition ◽  
Parallel Corpora ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Language Studies ◽  
Mapping Process ◽  
Pos Tagger

Recently, more data-driven approaches are demanding multilingual parallel resources primarily in the cross-language studies. To meet these demands, building multilingual parallel corpora are becoming the focus of many Natural Language Processing (NLP) scientific groups. Unlike monolingual corpora, the number of available multilingual parallel corpora is limited. In this paper, the MulTed, a corpus of subtitles extracted from TEDx talks is introduced. It is multilingual, Part of Speech (PoS) tagged, and bilingually sentence-aligned with English as a pivot language. This corpus is designed for many NLP applications, where the sentence-alignment, the PoS tagging, and the size of corpora are influential such as statistical machine translation, language recognition, and bilingual dictionary generation. Currently, the corpus has subtitles that cover 1100 talks available in over 100 languages. The subtitles are classified based on a variety of topics such as Business, Education, and Sport. Regarding the PoS tagging, the Treetagger, a language-independent PoS tagger, is used; then, to make the PoS tagging maximally useful, a mapping process to a universal common tagset is performed. Finally, we believe that making the MulTed corpus available for a public use can be a significant contribution to the literature of NLP and corpus linguistics, especially for under-resourced languages.

scholarly journals Part of Speech Tagging Using Hidden Markov Models

2020 ◽  
Vol 10 (1) ◽  
pp. 31-42
Author(s):  
Adrian Bărbulescu ◽  
Daniel I. Morariu
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Real Data ◽  
Good Precision ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Wide Range ◽  
N Gram ◽  
Decoding Speed

AbstractIn this paper, we present a wide range of models based on less adaptive and adaptive approaches for a PoS tagging system. These parameters for the adaptive approach are based on the n-gram of the Hidden Markov Model, evaluated for bigram and trigram, and based on three different types of decoding method, in this case forward, backward, and bidirectional. We used the Brown Corpus for the training and the testing phase. The bidirectional trigram model almost reaches state of the art accuracy but is disadvantaged by the decoding speed time while the backward trigram reaches almost the same results with a way better decoding speed time. By these results, we can conclude that the decoding procedure it’s way better when it evaluates the sentence from the last word to the first word and although the backward trigram model is very good, we still recommend the bidirectional trigram model when we want good precision on real data.

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

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.

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

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.

scholarly journals Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models

2016 ◽  
Vol 4 ◽  
pp. 245-257 ◽  
Author(s):  
Karl Stratos ◽  
Michael Collins ◽  
Daniel Hsu
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Benign Condition ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Interpretable Model ◽  
Log Linear ◽  
Hidden States

We tackle unsupervised part-of-speech (POS) tagging by learning hidden Markov models (HMMs) that are particularly well-suited for the problem. These HMMs, which we call anchor HMMs, assume that each tag is associated with at least one word that can have no other tag, which is a relatively benign condition for POS tagging (e.g., “the” is a word that appears only under the determiner tag). We exploit this assumption and extend the non-negative matrix factorization framework of Arora et al. (2013) to design a consistent estimator for anchor HMMs. In experiments, our algorithm is competitive with strong baselines such as the clustering method of Brown et al. (1992) and the log-linear model of Berg-Kirkpatrick et al. (2010). Furthermore, it produces an interpretable model in which hidden states are automatically lexicalized by words.

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