CasANER: Arabic Named Entity Recognition Tool

Arabic open information extraction system using dependency parsing

“…Current IE systems focus on analyzing the local context within individual sentences to extract entities and their relationships in a specific field while ignoring the redundant information that can be collectively [9]. In comparison with other languages, we observe a scarcity in efforts related to Arabic-based information extraction, which could be partly imputed to the complexity of Arabic makes it difficult to extract relations automatically [10].…”

“…The Arabic name entity recognition (NER) as a base for relation extraction applications has a significant share of this field research. Mesmia et al [9] proposed a system for recognizing the Arabic NER based on two transducers for analysis and synthesis. Darwish and Gao presented simple, effective, and language-independent approaches for improving NER in microblogs for Arabic as an example [11].…”

A Systematic Review of Islamic Terrorist Webpages and Keywords

“…To extract the terms from indexed words is indeed a challenging task due to the fact that several terms or combinations of terms are substantial to be considered at this stage. To extract the information from the terms, various studies were conducted based on different focuses such as; employing named entity recognition [11], bag-of-words (BOW) [12][13], n-grams [14], as well as lemmatization algorithms [15][16]. Further to that, in improving the extraction step, previous researchers utilized ontology-based extraction [16], semantic extraction [16], Arabic word sense disambiguation [17][18], semantic word embedding [19], and semantic relationships [20].…”

“…Dataset Used Baseline Extraction Outperform Extraction Domain [21] Dataset manual Khoja and Larkey stemmers Noun with verbs for stemmer Arabic clustering [14] TREC-2002 N-gram 3,4 and 5 level BOW Arabic classifiers [11] Arabic Wikipedia corpus Name entity and their our system Name entity and their our system are equivalent Arabic classification [12] collection of news articles BOW BOW with support vector machines as classifiers better than K nearest neighbor Arabic text categorization [19] Arabic TREC collection BOW word embedding similarities Information Retrieval [20] Dataset manual Ontology 'synonym, antonym, hypernym' and complex extraction…”

“…• The researchers presumed that the syntactic "wordlevel" or semantic "sense-level" are good for extracting information from the Islamic terrorists' web without overcoming the lack of words related to Islamic terrorists such as creating rich training of sense words extracting, and simultaneously avoiding the extraction of the insignificant features [11][12][13][14][15][16][17][18][19][20][21][22].…”