A Smart Framework for Multilingual Information Extraction

EasyChair Preprint 14391

Abstract

This study proposes a model for developing a smart framework for multilingual information extraction in natural language processing (NLP). The study explores cross-domain and cross-lingual transfer learning in addressing challenges associated with extracting valuable insights from diverse linguistic datasets using the baseline technique (language-gnostic and Language-specific models). Through innovative approaches and methodologies, the proposed Smart Framework, which supports Cross-domain and Cross-lingual Transfer Learning, enhances the efficiency and accuracy of information extraction, particularly Event Extraction (EE) across multiple languages. This research contributes to advancing multilingual NLP capabilities, enabling broader applications in various domains.

Keyphrases: Information Extraction, Multilingual Information Extraction, Natural Language Processing, cross-domain, cross-lingual transfer learning, multilingual event extraction

@booklet{EasyChair:14391,
  author    = {Israel Fianyi and Soonja Yeom},
  title     = {A Smart Framework for Multilingual Information Extraction},
  howpublished = {EasyChair Preprint 14391},
  year      = {EasyChair, 2024}}