🤖 AI Summary
This study addresses the bottleneck in acquiring lexical knowledge from Arabic–English dictionaries by proposing an integrated approach that combines n-gram modeling, keyword-in-context (KWIC) analysis, and rule-based information extraction. For the first time, this method systematically extracts morphological, syntactic, and semantic knowledge automatically from the Al-Mawrid bilingual dictionary. Leveraging punctuation patterns and heuristic strategies, the approach effectively identifies synonym sets, hyponymy-hypernymy relations, and domain-specific labels. Experimental results demonstrate high precision across all extraction tasks, with particularly strong recall for synonyms, thereby confirming that the Al-Mawrid dictionary encodes a rich repository of structured linguistic knowledge amenable to automated harvesting.
📝 Abstract
Natural language processing (NLP) applications need large and rich amount of linguistic knowledge. Furthermore, electronic language sources such as dictionaries, encyclopedia, and corpora became available. So, automatic methods are emerged to extract lexical information from those sources to overcome the knowledge acquisition bottleneck. We presented a method to automatically extract lexical information from a machine-readable version of the Arabic-English Al-Mawrid dictionary. We used n-gram analysis and key-word-in-context (KWIC) analysis to discover lexical patterns that manifest morphologic, syntactic, or semantic information. Then, we used hand-crafted rule-based information extraction to extract that information. Furthermore, we used punctuation marks and some heuristics to extract a set of synonyms in a subentry. This study registered high precision for all types of information, high recall for synonyms, and low recall for the other information. The study also showed that the Al-Mawrid has significant amount of derivations (morphologic information) and synonyms, domain labels, and hyponym/hypernym relations (semantic information).