High Performance Part-of-Speech Tagging of Bulgarian

2004 ◽  
pp. 246-255
Author(s):  
Veselka Doychinova ◽  
Stoyan Mihov
Keyword(s):  
High Performance ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Speech Tagging

scholarly journals Construction of the Uyghur Noun Morphological Re-Inflection Model Based on Hybrid Strategy

2019 ◽  
Vol 9 (4) ◽  
pp. 722 ◽  
Author(s):  
Muhetaer Munire ◽  
Xiao Li ◽  
Yating Yang
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
High Performance ◽  
Experimental Result ◽  
Hybrid Strategy ◽  
Language Generation ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Linguistic Rules ◽  
Speech Tagging

In this paper, a hybrid strategy of rules and statistics is employed to implement the Uyghur Noun Re-inflection model. More specifically, completed Uyghur sentences are taken as an input, and these Uyghur sentences are marked with part of speech tagging, and the nouns in the sentences remain the form of the stem. In this model, relevant linguistic rules and statistical algorithms are used to find the most probable noun suffixes and output the Uyghur sentences after the nouns are re-inflected. With rules of linguistics artificially summed up, the training corpora are formed by the human–machine exchange. The final experimental result shows that the Uyghur morphological re-inflection model is of high performance and can be applied to various fields of natural language processing, such as Uyghur machine translation and natural language generation.

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

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.

Mongolian part-of-speech tagging approach based on conditional random fields

2010 ◽  
Vol 30 (8) ◽  
pp. 2038-2040
Author(s):  
Yu-long YING ◽  
Miao LI ◽  
bala Wuda ◽  
Hai ZHU
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Speech Tagging

scholarly journals The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

2021 ◽  
Vol 184 ◽  
pp. 148-155
Author(s):  
Abdul Munem Nerabie ◽  
Manar AlKhatib ◽  
Sujith Samuel Mathew ◽  
May El Barachi ◽  
Farhad Oroumchian
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Approach ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
The Impact ◽  
Speech Tagging
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