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