OOV
Attia, M., Y. Samih, K. Shaalan, and J. van Genabith,
"The Floating Arabic Dictionary: An Automatic Method for Updating a Lexical Database through the detection and lemmatization of the Unknown Words",
The International Conference on Computational Linguistics (COLING), Mumbai, India, 15 December, 2012.
Abstract Unknown words, or out of vocabulary words (OOV), cause a significant problem to morphological analysers, syntactic parses, MT systems and other NLP applications. Unknown words make up 29 % of the word types in in a large Arabic corpus used in this study. With today's corpus sizes exceeding 109 words, it becomes impossible to manually check corpora for new words to be included in a lexicon. We develop a finite-state morphological guesser and integrate it with a machine-learning-based pre-annotation tool in a pipeline architecture for extracting unknown words, lemmatizing them, and giving them a priority weight for inclusion in a lexical database. The processing is performed on a corpus of contemporary Arabic of
1,089,111,204 words. Our method is tested on a manually-annotated gold standard and yields encouraging results despite the complexity of the task. Our work shows the usability of a highly non-deterministic morphological guesser in a practical and complex application.
