Part Of Speech Tagging System Research Articles

Comparative Analysis of Hidden Markov Model and Bidirectional Long Short-Term Memory for POS Tagging in Eastern Armenian

This scientific article introduces a novel POS tagging system specifically developed for the Eastern Armenian language. The primary objective of the study is to conduct a comparative analysis of two well-established methods for part-of-speech (POS) tagging: the hidden Markov model (HMM) coupled with the Viterbi algorithm, and an artificial neural network in the form of a recurrent neural network. The study places particular emphasis on the Eastern Armenian language and employs the ArmSpeech-POS Eastern Armenian part-of-speech tagged corpus for conducting comprehensive experiments and evaluations. POS tagging is a fundamental task in natural language processing (NLP) that involves assigning grammatical tags to words in a given text. Accurate POS tagging is crucial for various NLP applications, including machine translation, information retrieval, and sentiment analysis. The Viterbi algorithm is a well-established probabilistic method that utilizes a hidden Markov model (HMM) to determine the most likely sequence of POS tags. On the other hand, RNNs, a type of deep learning model, can capture complex patterns and dependencies in sequential data. Experimental results indicate that both methods achieve reasonable accuracy in POS tagging for Eastern Armenian. However, the RNN outperforms the Viterbi algorithm, exhibiting higher accuracy rates. This can be attributed to the RNN’s ability to capture long-range dependencies and learn intricate linguistic patterns. The article concludes by discussing the implications of the study’s findings and potential areas for further research. It emphasizes the significance of accurate POS tagging for improving NLP applications in Eastern Armenian and suggests exploring advanced neural network architectures and incorporating linguistic features to enhance POS tagging performance.

Part-of-Speech Tagging for Arabic Text using Particle Swarm Optimization and Genetic Algorithm

Background:Part of Speech (POS) Tagging is a process of defining the suitable part of speech for each word in the given context such as defining if a word is a verb, a noun or a particle. POS tagging is an important preprocessing step in many Natural Language Processing (NLP) applications such as question answering, text summarization, and information retrieval.Objective:The performance of NLP applications depends on the accuracy of POS taggers since assigning right tags for the words in a sentence enables the application to work properly after tagging. Many approaches have been proposed for the Arabic language, but more investigations are needed to improve the efficiency of Arabic POS taggers.Method:In this study, we propose a supervised POS tagging system for the Arabic language using Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) as well as Hidden Markov Model (HMM). The tagging process is considered as an optimization problem and illustrated as a swarm which consists of group of particles. Each particle represents sequence of tags. The PSO algorithm is applied to find the best sequence of tags which represent the correct tags of the sentence. The genetic operators: crossover and mutation are used to find personal best, global best, and velocity of the PSO algorithm. HMM is used to find the fitness of particles in the swarm.Results :The performance of the proposed approach is evaluated on the KALIMAT dataset which consists of 18 million words and a tag set consists of 45 tags which covers all Arabic POS tags. The proposed tagger achieved an accuracy of 90.5%.Conclusion:Experimental results revealed that the proposed tagger achieved promising results compared to four existing approaches. Other approaches can identify only three tags: noun, verb and particle. Also, the accuracy for some tags are outperformed those achieved by other approaches.

Generating Summaries Through Unigram and Bigram

This article describes a new method for generating extractive summaries directly via unigram and bigram extraction techniques. The methodology uses the selective part of speech tagging to extract significant unigrams and bigrams from a set of sentences. Extracted unigrams and bigrams along with other features are used to build a final summary. A new selective rule-based part of speech tagging system is developed that concentrates on the most important parts of speech for summarizations: noun, verb, and adjective. Other parts of speech such as prepositions, articles, adverbs, etc., play a lesser role in determining the meaning of sentences; therefore, they are not considered when choosing significant unigrams and bigrams. The proposed method is tested on two problem domains: citations and opinosis data sets. Results show that the proposed method performs better than Text-Rank, LexRank, and Edmundson summarization methods. The proposed method is general enough to summarize texts from any domain.

Disambiguation using joint entropy in part of speech of written Myanmar text

Myanmar language also known as Burmese language is a free order of word language and syntactic patterns of one word can vary based on the position and the structure in the sentence. There are many ambiguous part of speech (POS) tags on one word in the sentence of Myanmar text. This research work presents disambiguation for the POS of the words in written Myanmar text. We aim at removing this ambiguity on Myanmar word and assigning single POS to each word of sentence. This is demonstrated on the following ideas: (i) input the sentence and segmented into words using syllable segmentation rules and forward maximum matching approach with monolingual Myanmar dictionary and (ii) apply the Joint Entropy (JE) for POS ambiguous for each word in the sentence with monolingual Myanmar tagged corpus. Joint probability value could be given the useful and accurate disambiguation of POS for free order and structure of words in Myanmar text. The monolingual Myanmar tagged corpus and tagged dictionary are created including 620 sentences and 15,000 words, respectively. This study attempts practical word segmentation and POS tagging system which can really overcome bottleneck of the machine translation system for Myanmar to other languages and research activities related to natural language processing (NLP).

Multilingual POS tagging by a composite deep architecture based on character-level features and on-the-fly enriched Word Embeddings

Natural Language Processing (NLP) field is taking great advantage from adopting models and methodologies from Artificial Intelligence. In particular, Part-Of-Speech (POS) tagging is a building block for many NLP applications. In this paper, a POS tagging system based on a deep neural network is proposed. It is made of a static and task-independent pre-trained model for representing words semantics enriched by morphological information, by approximating the Word Embedding representation learned from an unlabelled corpus by the fastText model, so as to handle consistently common and known words as well as rare and Out-of-Vocabulary words. A character-level representation of words is dynamically learned according to the POS tagging task, and is concatenated to the previous one. This joint representation is fed to the main network, comprising a Bi-LSTM layer, trained to associate a sequence of tags to a sequence of words. The effectiveness of the contributions of the proposed system with respect to the state-of-the-art is proven by an extensive experimental campaign, which provides evidence that improvements are gained in POS tagging accuracy by using Word Embeddings enriched with morphological information, by estimating embeddings for both known and unknown words, and by concatenating Word Embeddings with character-level information of the same size. Similar trends are obtained for two languages of different characteristics, namely English and Italian: in both cases, the overall accuracy on the POS tagging test set was increased with respect to the most advanced existing systems, with particular improvements on the accuracy of Out-of-Vocabulary words. Finally, the method has a general basis, and could be proficiently used for all languages, particularly for those showing a wide morphological richness.

The Development of Indonesian POS Tagging System for Computer-aided Independent Language Learning

Word processing tool is a basic need in learning a language. One of the word processors needed by a language learner is part of speech (POS) tagging. While many POS Tagging tools for Indonesian language have been developed, no systems have been addressed specifically for language learners. This paper presents a study on an Indonesian part of speech (POS) tagging system developed as one of word processing tools for language learners. We use resources from previous Indonesian POS tagging research, such as MorphInd for the morphological analysis and IPOSTagger for part of speech tagging. Objective and subjective tests are employed to evaluate this system. In the objective test the part of speech tagging results use a system model developed from IPOSTagger in combination with MorphInd as the morphological analyzer, and compared with the results of part of speech tagging produced from the original IPOSTagger system model. The results show that the part of speech tagging accuracy using this system model is higher than other models. For its subjective evaluation, Mean Opinion Score (MOS) is used to the 24 participating respondents. The MOS results obtained reach 3,61 for test-1, 3,87 for test-2, and 3,72 for test-3. From the results, we expect that this POS tagging system could be used to help language learners in their Indonesian language self-learning process.

Stagger: an Open-Source Part of Speech Tagger for Swedish

This work presents Stagger, a new open-source part of speech tagger for Swedish based on the Averaged Perceptron. By using the SALDO morphological lexicon and semi-supervised learning in the form of Collobert andWeston embeddings, it reaches an accuracy of 96.4% on the standard Stockholm-Umeå Corpus dataset, making it the best single part of speech tagging system reported for Swedish. Accuracy increases to 96.6% on the latest version of the corpus, where the annotation has been revised to increase consistency. Stagger is also evaluated on a new corpus of Swedish blog posts, investigating its out-of-domain performance.

ITagger: Part-of-Speech Tagging Based on SBCB Learning Algorithm

The problem of part-of-speech (POS) tagging or disambiguation is a practical issue in natural language processing (NLP) community, especially in the development of a machine translation system. The performance of POS tagging system may interference the subsequent analytical tasks in the translation process, and thereafter affects the overall translation quality. This paper presents a novel POS tagging system, iTagger, which is developed based on Selecting Base Classifiers on Bagging (SBCB) learning algorithm. In this work, the POS tagging task is regarded as a classification problem. Features such as the surrounding context of ambiguous candidates, n-gram information, lexical items and linguistic clues are used and automatically extracted from the annotated corpus. The proposed system has been compared against two state-of-the-art tagging methods, Hidden Markov Model (HMM) and Maximum Entropy. The empirical results conducted on the corpora of (English) Brown corpus, (Portuguese) Tycho Brahe corpus and the Chinese Tree Bank corpus reveal the competitiveness of iTagger. Moreover, the iTagger has been developed and released to the public as library and tool for various development and application purposes.

NB 모델을 이용한 형태소 복원

한국어는 교착어이어서 형태소 분석 없이 품사 부착이 어려울 뿐 아니라 형태소를 분석할 때 다양한 어형 변화가 복원되어야 한다. 이것은 한국어 형태소 분석의 고질적인 문제 중 하나이며, 주로 규칙을 이용해서 해결한다. 규칙을 이용할 경우 주어진 문맥에 가장 적합한 복원을 어려워 여러 형태의 모호성을 생성하며, 이는 품사 부착에 의해서 해결된다. 본 논문에서는 이 문제를 기계학습 방법(Na<TEX>$\ddot{i}$</TEX>ve Bayes 모델)을 이용하여 해결한다. 기계학습 모델의 입력 자질은 어형 변화가 발생하는 주변 음절이며 출력 범주는 복원된 음절이다. ETRI 구문 말뭉치를 이용한 실험에서 제안된 형태소 복원 모델을 사용한 형태소 단위의 품사 부착 성능은 97.5%의 <TEX>$F_1$</TEX>점수를 보였으며 이 모델이 형태소 복원에 매우 유용함을 알 수 있었다. In Korean, spelling change in various forms must be recovered into base forms in morphological analysis as well as part-of-speech (POS) tagging is difficult without morphological analysis because Korean is agglutinative. This is one of notorious problems in Korean morphological analysis and has been solved by morpheme recovery rules, which generate morphological ambiguity resolved by POS tagging. In this paper, we propose a morpheme recovery scheme based on machine learning methods like Na<TEX>$\ddot{i}$</TEX>ve Bayes models. Input features of the models are the surrounding context of the syllable which the spelling change is occurred and categories of the models are the recovered syllables. The POS tagging system with the proposed model has demonstrated the <TEX>$F_1$</TEX>-score of 97.5% for the ETRI tree-tagged corpus. Thus it can be decided that the proposed model is very useful to handle morpheme recovery in Korean.

Syllable-Pattern-Based Unknown-Morpheme Segmentation and Estimation for Hybrid Part-of-Speech Tagging of Korean

Most errors in Korean morphological analysis and part-of-speech (POS) tagging are caused by unknown morphemes. This paper presents a syllable-pattern-based generalized unknown-morpheme-estimation method with POSTAG (POStech TAGger), 1 which is a statistical and rule-based hybrid POS tagging system. This method of guessing unknown morphemes is based on a combination of a morpheme pattern dictionary that encodes general lexical patterns of Korean morphemes with a posteriori syllable trigram estimation. The syllable trigrams help to calculate lexical probabilities of the unknown morphemes and are utilized to search for the best tagging result. This method can guess the POS tags of unknown morphemes regardless of their numbers and/or positions in an eojeol (a Korean spacing unit similar to an English word), which is not possible with other systems for tagging Korean. In a series of experiments using three different domain corpora, the system achieved a 97% tagging accuracy even though 10% of the morphemes in the test corpora were unknown. It also achieved very high coverage and accuracy of estimation for all classes of unknown morphemes. 1 The binary code of POSTAG is open to the public for research and evaluation purposes at http://nlp.postech.ac.kr/. Follow the link OpenResources→DownLoad.