Arabic keyphrases extraction using a hybrid of statistical and machine learning methods

A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning

“…Ghaleb Ali and Omar [62], proposed a key phrase extraction method that combines several key phrase extraction methods with ML algorithms. The output from the key phrase extraction methods is used as features to the ML algorithms.…”

Features based approach for indexation and representation of unstructured Arabic documents

“…Nabil et al [26] Combining linguistic methods with statistical methods Ali et al [27] enrich statistical and linguistic information Mihalcea et al [28] A Graph based ranking algorithm El-Beltagy et al [29] Consider the position of the first occurrence of any given phrase Elshishtawy et al [30] Combining the linguistic knowledge and the machine learning techniques Najadat et al [31] Phrase frequency (PF), summation of phrase terms frequencies (Tf), PFIDF (Phrase Frequency -Inverse Document Frequecy), Phrase Position, Title Threshold and phrase distribution Liu et al [32] The best keyphrase must be: understandable, semantically relevant with the document and have high coverage of the whole document Sarkar et al [33] A candidate keyphrase is considered as a sequence of words that does not contain neither punctuations nor stop words, and then this sequence is broken into smaller phrases…”