Enhancing retrieval and novelty detection for arabic text using sentence level information pattern

Novelty detection is already used in many Natural Processing Language (NLP) applications, such as information retrieval systems, Web search engines, text summarization, question answering systems...etc. This study aims to detect novel Arabic sentence level information patterns. The Length Adjusted (LA) model is based on sentence level information patterns is used, which depends on the sentence length. Test results show a significant improvement in the performance of novelty detection for Arabic texts in terms of precision at top ranks.