Arabic Keyphrase Extraction: Enhancing Deep Learning Models with Pre-trained Contextual Embedding and External Features
Randah Alharbi, Husni Al-Muhtasab · 2022
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.