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21	Sumptuous Soul: The Music of Donny Hathaway Everything is Everything Donny Hathaway, 1970 Hicks, Keisha 17 June 2014 (has links) No description available. African American Studies African Americans American Studies History Sociology Donny Hathaway Roberta Flack Stevie Wonder Marvin Gaye Soul music Gospel music Enslaved Persons slavery, Jazz Ric Powell Trio Howard University Dizzy Gillespie Chano Pozo Lalah Hathaway Arif Mardin Atlantic Records Jerry Wexler
22	Stylometry: Quantifying Classic Literature For Authorship Attribution : - A Machine Learning Approach Yousif, Jacob, Scarano, Donato January 2024 (has links) Classic literature is rich, be it linguistically, historically, or culturally, making it valuable for future studies. Consequently, this project chose a set of 48 classic books to conduct a stylometric analysis on the defined set of books, adopting an approach used by a related work to divide the books into text segments, quantify the resulting text segments, and analyze the books using the quantified values to understand the linguistic attributes of the books. Apart from the latter, this project conducted different classification tasks for other objectives. In one respect, the study used the quantified values of the text segments of the books for classification tasks using advanced models like LightGBM and TabNet to assess the application of this approach in authorship attribution. From another perspective, the study utilized a State-Of-The-Art model, namely, RoBERTa for classification tasks using the segmented texts of the books instead to evaluate the performance of the model in authorship attribution. The results uncovered the characteristics of the books to a reasonable degree. Regarding the authorship attribution tasks, the results suggest that segmenting and quantifying text using stylometric analysis and supervised machine learning algorithms is practical in such tasks. This approach, while showing promise, may still require further improvements to achieve optimal performance. Lastly, RoBERTa demonstrated high performance in authorship attribution tasks. Authorship Attribution Classic Literature Analysis Clustering Data Science Deep Learning Feature Engineering Feature Extraction Gradient Descent K-Means LightGBM Machine Learning Multiclass Classification NLP Neural Network RoBERTa Stylometric Analysis Stylometry TabNet t-SNE Text Mining Transformer Models Computer Sciences Datavetenskap (datalogi) Computer and Information Sciences Data- och informationsvetenskap
23	Large-Context Question Answering with Cross-Lingual Transfer Sagen, Markus January 2021 (has links) Models based around the transformer architecture have become one of the most prominent for solving a multitude of natural language processing (NLP)tasks since its introduction in 2017. However, much research related to the transformer model has focused primarily on achieving high performance and many problems remain unsolved. Two of the most prominent currently are the lack of high performing non-English pre-trained models, and the limited number of words most trained models can incorporate for their context. Solving these problems would make NLP models more suitable for real-world applications, improving information retrieval, reading comprehension, and more. All previous research has focused on incorporating long-context for English language models. This thesis investigates the cross-lingual transferability between languages when only training for long-context in English. Training long-context models in English only could make long-context in low-resource languages, such as Swedish, more accessible since it is hard to find such data in most languages and costly to train for each language. This could become an efficient method for creating long-context models in other languages without the need for such data in all languages or pre-training from scratch. We extend the models’ context using the training scheme of the Longformer architecture and fine-tune on a question-answering task in several languages. Our evaluation could not satisfactorily confirm nor deny if transferring long-term context is possible for low-resource languages. We believe that using datasets that require long-context reasoning, such as a multilingual TriviaQAdataset, could demonstrate our hypothesis’s validity. Long-Context Multilingual Model Longformer XLM-R Longformer Long-term Context Extending Context Extend Context Large-Context Long-Context Large Context Long Context Cross-Lingual Multi-Lingual Cross Lingual Multi Lingual QA Question-Answering Question Answering Transformer model Machine Learning Transfer Learning SQuAD Memory Transfer Learning Long-Context Long Context Efficient Monolingual Multilingual QA model Language Model Huggingface BERT RoBERTa XLM-R mBERT Multilingual BERT Efficient Transformers Reformer Linformer Performer Transformer-XL Wikitext-103 TriviaQA HotpotQA WikiHopQA VINNOVA Peltarion AI LM MLM Deep Learning Natural Language Processing NLP Attention Transformers Transfer Learning Datasets Computer and Information Sciences Data- och informationsvetenskap

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