Arabic Keyphrase Extraction: Enhancing Deep Learning Models with Pre-trained Contextual Embedding and External Features
Year: 2022
Type: conference-paper
Abstract: Keyphrase extraction is essential to many Information retrieval (IR) and Natural language Processing (NLP) tasks such as summarization and indexing.This study investigates deep learning approaches to Arabic keyphrase extraction.We address the problem as sequence classification and create a Bi-LSTM model to classify each sequence token as either part of the keyphrase or outside of it.We have extracted word embeddings from two pre-trained models, Word2Vec and BERT.Moreover, we have investigated the effect of incorporating linguistic, positional, and statistical features with word embeddings on performance.Our best-performing model has achieved 0.45 F1score on ArabicKPE dataset when combining linguistic and positional features with BERT embedding.
Authors: Randah Alharbi, Husni Al-Muhtasab
Institution: King Fahd University of Petroleum and Minerals
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4385572822