Persian-WSD-Corpus: A Sense Annotated Corpus for Persian All-words Word Sense Disambiguation
This addresses a gap for Persian NLP researchers by providing the first standard test set for all-words WSD in Persian, though it is incremental as it adapts existing methods to a new language.
The authors tackled the lack of a standard benchmark for Persian all-words word sense disambiguation by introducing SBU-WSD-Corpus, a manually annotated dataset with 3371 sense-annotated words from 19 documents, and provided baseline evaluations for future research.
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.