أداة تمييز جزء من الكلام للبحث عن التناغم التكيفي لمربع لغة همونغ كوربوس
محتوى المقالة الرئيسي
الملخص
يعتبر أداء النماذج المبنية على البيانات ضعيفًا فيما يتعلق بمشكلات وضع علامات على جزء من الكلام لمربع لغة الهمونغ ، وهي مجموعة منخفضة الموارد. تصمم هذه الورقة وظيفة تقييم الوزن لتقليل تأثير الكلمات غير المعروفة. يقترح خوارزمية بحث متناغمة محسنة باستخدام الروليت واستراتيجيات التقييم المحلية للتعامل مع مشكلة وضع علامات على جزء من الكلام الهمونغ. أظهرت التجربة أن متوسط دقة النموذج المقترح هو 6%، 8% أكثر من نماذج HMM وBiLSTM-CRF، على التوالي. وفي الوقت نفسه، فإن متوسط F1 للنموذج المقترح هو أيضًا 6%، أي 3% أكثر من طرازي HMM وBiLSTM-CRF، على التوالي.
Received 27/09/2023
Revised 10/02/2024
Accepted 12/02/2024
Published 25/02/2024
تفاصيل المقالة
هذا العمل مرخص بموجب Creative Commons Attribution 4.0 International License.
كيفية الاقتباس
المراجع
Liu B, Du J, Nie B, et al. Part-of-speech Tagging of Traditional Chinese Medicine Diagnosis Ancient Prose Based on Second-order HMM. Computer Engineering. 2017 Jul;43(7):211-216. Chinese. https://doi.org/10.3969/j.issn.1000-3428.2017.07.035.
Warjri S, Pakray P, Lyngdoh S A, et al. Part-of-speech (pos) tagging using conditional random field (crf) model for khasi corpora. Int. J. Speech Technol.. 2021 Jun;24(4):853-864. https://doi.org/10.1007/s10772-021-09860-w
Awwalu J, Abdullahi S E Y, Evwiekpaefe A E. Parts of speech tagging: a review of techniques. FJS. 2020 Jun;4(2):712-721. https://doi.org/10.33003/fjs-2020-0402-325
AlKhwiter W, Al-Twairesh N. Part-of-speech tagging for Arabic tweets using CRF and Bi-LSTM. Computer Speech & Language. 2021 Aug;65:101138. https://doi.org/10.1016/j.csl.2020.101138
Eskander R, Muresan S, Collins M. Unsupervised cross-lingual part-of-speech tagging for truly low-resource scenarios. Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP). 2020 Nov;4820-4831. https://doi.org/10.18653/v1/2020.emnlp-main.391
Magistry P, Ligozat A L, Rosset S. Exploiting languages proximity for part-of-speech tagging of three French regional languages. LRE. 2019 Apr;53:865-888. https://doi.org/10.1007/s10579-019-09463-7
Li H C, Mo L P, Zhou K Q. A Part-Of-Speech Tagging Approach for Chinese-Hmong Mixed Text. IOP Conference Series: Materials Science and Engineering. 2020 Feb;864(1):012064. https://doi.org/10.1088/1757-899X/864/1/012064
Heid S, Wever M, Hüllermeier E. Reliable part-of-speech tagging of historical corpora through set-valued prediction. arXiv preprint arXiv:2008.01377. 2020 Aug. https://doi.org/10.48550/arXiv.2008.01377
Geem Z W, Kim J H, Loganathan G V. A new heuristic optimization algorithm: harmony search. Simulation. 2001 Feb;76(2):60-68. https://doi.org/10.1177/00375497010760020
Qin F, Zain A M, Zhou K Q. Harmony search algorithm and related variants: A systematic review[J]. Swarm Evol. Comput. .2022: 101126. https://doi.org/10.1016/j.swevo.2022.101126
Shaqfa M, Orbán Z. Modified parameter-setting-free harmony search (PSFHS) algorithm for optimizing the design of reinforced concrete beams. Struct Multidiscipl Optim .. 2019 Mar;60:999-1019. https://doi.org/10.1007/s00158-019-02252-4
Song Y, Pan Q K, Gao L, et al. Improved non-maximum suppression for object detection using harmony search algorithm. Appl. Soft Comput.. 2019 Aug; 81:105478. https://doi.org/10.1016/j.asoc.2019.05.005
Christodoulou C A, Vita V, Seritan G C, et al. A harmony search method for the estimation of the optimum number of wind turbines in a wind farm. Energies. 2020 Jun;13(11):2777. https://doi.org/10.3390/en13112777
Mistarihi M Z, Okour R A, Magableh G M, et al. Integrating advanced harmony search with fuzzy logic for solving buffer allocation problems. Arab J Sci Eng. 2020 Jan;45:3233-3244. https://doi.org/10.1007/s13369-020-04348-2
AL-Jumaili A S. A hybrid method of linguistic and statistical features for Arabic sentiment analysis[J]. Baghdad Sci. J.2020; 17(1 (Suppl.)): 0385-0385. https://doi.org/10.21123/bsj.2020.17.1(Suppl.).0385
Kang D W, Mo L P, Wang F L, et al. Adaptive harmony search algorithm utilizing differential evolution and opposition-based learning[J]. Math Biosci Eng. 2021; 18(4): 4226-4246. https://doi.org/10.3934/mbe.2021212
WANG Zhongmiao, LIU Jun. Computing the Stationary Distribution of Absorbing Markov Chains with One Eigenvector of Diagonalizable Transition Matrices. Chinese Journal of Applied Probability And Statistics. 2020, 36(2): 123-137. https://doi.org/10.3969/j.issn.1001-4268.2020.02.002
Naif O S, Mohammed I J. WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO)[J]. Baghdad Sci. J.2022;19(3): 0605-0605. https://doi.org/10.21123/bsj.2022.19.3.0605