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Fine Tuning Named Entity Extraction Models for the Fantasy Domain.

, и . CoRR, (2024)

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Colin De Silva

Maria Angelica De Souza Silva

Pedro Augusto De Souza e Silva

Manuel Silva

Gevson Silva Andrade

 

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SigmaLaw PBSA - A Deep Learning Approach For Aspect Based Sentiment Analysis in Legal Opinion Texts., , , , , и . J. Data Intell., 3 (1): 101-115 (2022)Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning., , , , , и . WASSA@EMNLP, стр. 260-265. Association for Computational Linguistics, (2018)Multi-Document Summarization: A Comparative Evaluation., , и . ICIIS, стр. 19-24. IEEE, (2023)Effective Approach to Develop a Sentiment Annotator For Legal Domain in a Low Resource Setting., , , и . PACLIC, стр. 252-260. Association for Computational Linguistics, (2020)Learning Sentence Embeddings In The Legal Domain with Low Resource Settings., , , , , и . PACLIC, стр. 494-502. De La Salle University, (2022)Shift-of-Perspective Identification within Legal Cases., , , , , и . ASAIL@ICAIL, том 2385 из CEUR Workshop Proceedings, CEUR-WS.org, (2019)Legal Document Retrieval using Document Vector Embeddings and Deep Learning., , , , , , и . CoRR, (2018)Legal Case Winning Party Prediction With Domain Specific Auxiliary Models., , , , и . ROCLING, стр. 205-213. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), (2022)Synergistic union of Word2Vec and lexicon for domain specific semantic similarity., , , , , , и . ICIIS, стр. 1-6. IEEE, (2017)Semantic Oppositeness Embedding Using an Autoencoder-Based Learning Model., и . DEXA (1), том 11706 из Lecture Notes in Computer Science, стр. 159-174. Springer, (2019)