Hossein Rouhizadeh - Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
Mehrnoush Shamsfard - Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
Vahide Tajalli - University of Tehran, Tehran, Iran

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 ۱۹ Persian documents in different domains such as Sports, Science, Arts, etc. It includes ۵۸۹۲ content words of Persian running text and ۳۳۷۱ manually sense annotated words (۲۰۷۳ nouns, ۵۶۶ verbs, ۶۱۰ adjectives, and ۱۲۲ adverbs). Providing baselines for future studies on the Persian All-words WSD task, we evaluate several WSD models on SBU-WSD-Corpus.

Word Sense Disambiguation, WSD Corpus, All-words WSD, Persian Language Processing