scholarly journals Markov random field based English part-of-speech tagging system

1996 ◽  
Author(s):  
Sung-Young Jung ◽  
Young C. Park ◽  
Key-Sun Choi ◽  
Youngwhan Kim
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Markov Random ◽  
Speech Tagging ◽  
Tagging System

Part-Of-Speech Tagging for Mizo Language Using Conditional Random Field

2021 ◽  
Vol 25 (4) ◽  
Author(s):  
Morrel VL Nunsanga ◽  
Partha Pakray ◽  
C. Lallawmsanga ◽  
L. Lolit Kumar Singh
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Speech Tagging

Generating Summaries Through Unigram and Bigram

2020 ◽  
Vol 15 (1) ◽  
pp. 64-74
Author(s):  
Nesreen Mohammad Alsharman ◽  
Inna V. Pivkina
Keyword(s):  
New Method ◽  
Data Sets ◽  
Extraction Techniques ◽  
Rule Based ◽  
Parts Of Speech ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Speech Tagging ◽  
Tagging System ◽  
Better Than

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.

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

2017 ◽  
Vol 12 (11) ◽  
pp. 138
Author(s):  
Muljono Muljono ◽  
Umriya Afini ◽  
Catur Supriyanto ◽  
Raden Arief Nugroho
Keyword(s):  
Language Learners ◽  
Word Processing ◽  
Subjective Evaluation ◽  
Language Learner ◽  
System Model ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Speech Tagging ◽  
Tagging System

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.

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