Deep Learning for Multilingual POS Tagging

Toleu, Alymzhan; Tolegen, Gulmira; Mussabayev, Rustam

doi:10.1007/978-3-030-63119-2_2

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…These models are trained on English data and currently work only on English text. Single models handling multiple languages are currently being developed and have yet to achieve strong performance on a wide array of languages (Toleu et al., 2020). Therefore, models for each required language are still needed.…”

Section: Materials and Methods: Pipelinementioning

confidence: 99%

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Automated retrieval of information on threatened species from online sources using machine learning

Kulkarni

Minin

2021

Methods Ecol Evol

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As resources for conservation are limited, gathering and analyzing information from digital platforms can help investigate the global biodiversity crisis in a cost-efficient manner. Development and application of methods for automated content analysis of digital data sources are especially important in the context of investigating human-nature interactions.2. In this study, we introduce novel application methods to automatically collect and analyze textual data on species of conservation concern from digital platforms. An end to end pipeline is constructed that begins from searching and downloading news articles about species listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) along with news articles from specific Twitter handles and proceeds with implementing natural language processing and machine learning methods to filter and retain only relevant articles. A crucial aspect here is the automatic annotation of training data, which can be challenging in many machine learning applications. A Named Entity Recognition model is then used to extract additional relevant information for each article.3. The data collected over a one month period included 15,088 articles focusing on 585 species listed in Appendix I of CITES. The accuracy of the neural network to detect relevant articles was 95.91% while the Named Entity recognition model helped extract information on prices, location, and quantities of traded animals and plants. A regularly updated database, which can be queried and analysed for various research purposes and to inform conservation decision-making, is generated by the system. 4. The results demonstrate that natural language processing can be used successfully to extract information from digital text content. The proposed methods can be applied to multiple digital data platforms at the same time and used to investigate human-nature interactions in conservation science and practice.

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Section: Materials and Methods: Pipelinementioning

confidence: 99%

mentioning

confidence: 99%

Automated retrieval of information on threatened species from online sources using machine learning

Kulkarni

Minin

2021

Methods Ecol Evol

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Indonesian Part-of-Speech Tagger: A Comparative Study

Alfian,

Yuhana,

Siahaan

2023

2023 10th International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA)

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Contrastive Learning for Morphological Disambiguation Using Large Language Models in Low-Resource Settings

Tolegen,

Toleu,

Mussabayev

2024

Applied Sciences

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In this paper, a contrastive learning approach for morphological disambiguation (MD) using large language models (LLMs) is presented. A contrastive loss function is introduced for training the approach, which reduces the distance between the correct analysis and contextual embeddings while maintaining a margin between correct and incorrect embeddings. One of the aims of the paper is to analyze the effects of fine-tuning an LLM on MD in morphologically complex languages (MCLs) with special reference to low-resource languages such as Kazakh, as well as Turkish. Another goal of the paper is to consider various distance measures for this contrastive loss function, aiming to achieve better results when performing disambiguation by computing the distance between the context and the analysis embeddings. The existing approaches for morphological disambiguation, such as HMM-based and feature-engineering approaches, have limitations in modeling long-term dependencies and in the case of large, sparse tagsets. These challenges are mitigated in the proposed approach by leveraging LLMs, thus achieving better accuracy in handling the cases of ambiguity and OOV tokens without the need to rely on other features. Experiments were conducted on three datasets for two MCLs, Kazakh and Turkish—the former is a typical low-resource language. The results revealed that the proposed approach with contrastive loss improves MD performance when integrated with knowledge from large language models.

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Deep Learning for Multilingual POS Tagging

Cited by 3 publications

References 16 publications

Automated retrieval of information on threatened species from online sources using machine learning

Automated retrieval of information on threatened species from online sources using machine learning

Indonesian Part-of-Speech Tagger: A Comparative Study

Contrastive Learning for Morphological Disambiguation Using Large Language Models in Low-Resource Settings

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