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Word Sense Disambiguation (WSD) is a long-standing task in Natural LanguageProcessing(NLP) that aims to automatically identify the most relevant meaningof the words in a given context. Developing standard WSD test collections canbe mentioned as an important prerequisite for developing and evaluatingdifferent WSD systems in the language of interest. Although many WSD testcollections have been developed for a variety of languages, no standardAll-words WSD benchmark is available for Persian. In this paper, we addressthis shortage for the Persian language by introducing SBU-WSD-Corpus, as thefirst standard test set for the Persian All-words WSD task. SBU-WSD-Corpus ismanually annotated with senses from the Persian WordNet (FarsNet) senseinventory. To this end, three annotators used SAMP (a tool for sense annotationbased on FarsNet lexical graph) to perform the annotation task. SBU-WSD-Corpusconsists of 19 Persian documents in different domains such as Sports, Science,Arts, etc. It includes 5892 content words of Persian running text and 3371manually sense annotated words (2073 nouns, 566 verbs, 610 adjectives, and 122adverbs). Providing baselines for future studies on the Persian All-words WSDtask, we evaluate several WSD models on SBU-WSD-Corpus. The corpus is publiclyavailable at https://github.com/hrouhizadeh/SBU-WSD-Corpus.

Persian-WSD-Corpus: A Sense Annotated Corpus for Persian All-words Word Sense Disambiguation