Fully unsupervised word translation from cross-lingual word embeddings especially for healthcare professionals

Chauhan, Shweta; Saxena, Shefali; Daniel, Philemon

doi:10.1007/s13198-021-01182-z

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…These advancements have revolutionized the field, enabling more accurate and nuanced translations between English and other languages [10]. One of the key drivers behind this progress is the development and adoption of neural machine translation (NMT) models, which have significantly improved the quality and fluency of translations by leveraging deep learning techniques [11]. These models are adept at capturing complex grammatical structures, idiomatic expressions, and linguistic nuances, thereby producing translations that closely mimic natural language usage [12].…”

Section: Introductionmentioning

confidence: 99%

Cross-Language Translation and Comparative Study of English Literature Using Machine Translation Algorithms

Shanshan Wang

2024

jes

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Cross-language processing in English literature involves the translation and analysis of literary texts from English into other languages or vice versa. This multidimensional task encompasses various aspects, including language translation, cultural adaptation, and literary interpretation. Through cross-language processing, literary works originally written in English can reach a wider audience, enabling individuals from diverse linguistic backgrounds to access and appreciate the richness of English literature. This paper presents an innovative approach to language processing tasks through the integration of Ant Swarm Domain Statistical Machine Learning (ASDS-ML). Leveraging principles of swarm intelligence and statistical learning techniques, ASDS-ML offers a robust framework for addressing challenges in language translation and classification. In the domain of translation, ASDS-ML demonstrates promising results in achieving accurate and nuanced translations across diverse language pairs, while also exhibiting adaptability to varying linguistic contexts. Furthermore, ASDS-ML showcases its effectiveness in text classification tasks, accurately categorizing instances across multiple classes with high precision and recall. In language translation tasks, ASDS-ML achieves an average BLEU score of 0.85 across multiple language pairs, outperforming baseline methods by 10%. Additionally, in text classification tasks, ASDS-ML achieves an average accuracy of 0.92 across ten different classes, surpassing existing approaches by 5%.

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Section: Introductionmentioning

confidence: 99%

Cross-Language Translation and Comparative Study of English Literature Using Machine Translation Algorithms

Shanshan Wang

2024

jes

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“…These important words are known as named entities (NEs), and the task is known as named-entity recognition (NER). The task of named-entity recognition is important because it further helps in different natural language processing (NLP) tasks such as question answering [2], machine translation [3], relation extraction [4], and many more [5,6]. It is often the case that one entity resides within or overlaps with another entity.…”

Section: Introductionmentioning

confidence: 99%

BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling

et al. 2022

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Named-entity recognition (NER) is one of the primary components in various natural language processing tasks such as relation extraction, information retrieval, question answering, etc. The majority of the research work deals with flat entities. However, it was observed that the entities were often embedded within other entities. Most of the current state-of-the-art models deal with the problem of embedded/nested entity recognition with very complex neural network architectures. In this research work, we proposed to solve the problem of nested named-entity recognition using the transfer-learning approach. For this purpose, different variants of fine-tuned, pretrained, BERT-based language models were used for the problem using the joint-labeling modeling technique. Two nested named-entity-recognition datasets, i.e., GENIA and GermEval 2014, were used for the experiment, with four and two levels of annotation, respectively. Also, the experiments were performed on the JNLPBA dataset, which has flat annotation. The performance of the above models was measured using F1-score metrics, commonly used as the standard metrics to evaluate the performance of named-entity-recognition models. In addition, the performance of the proposed approach was compared with the conditional random field and the Bi-LSTM-CRF model. It was found that the fine-tuned, pretrained, BERT-based models outperformed the other models significantly without requiring any external resources or feature extraction. The results of the proposed models were compared with various other existing approaches. The best-performing BERT-based model achieved F1-scores of 74.38, 85.29, and 80.68 for the GENIA, GermEval 2014, and JNLPBA datasets, respectively. It was found that the transfer learning (i.e., pretrained BERT models after fine-tuning) based approach for the nested named-entity-recognition task could perform well and is a more generalized approach in comparison to many of the existing approaches.

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Adopting machine translation in the healthcare sector: A methodological multi-criteria review

Zappatore,

Ruggieri

2024

Computer Speech & Language

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Fully unsupervised word translation from cross-lingual word embeddings especially for healthcare professionals

Cited by 9 publications

References 22 publications

Cross-Language Translation and Comparative Study of English Literature Using Machine Translation Algorithms

Cross-Language Translation and Comparative Study of English Literature Using Machine Translation Algorithms

BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling

Adopting machine translation in the healthcare sector: A methodological multi-criteria review

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