Knowledge Based Word Sense Disambiguation with Distributional Semantic Expansion for the Persian Language
Hossein Rouhizadeh, Mehrnoush Shamsfard, Masoud Rouhizadeh · 2020
Word Sense Disambiguation (WSD) can be the key component of downstream NLP applications. Existing WSD methods and systems are mostly developed and evaluated on English and low-resource languages such as Persian have not been well studied. In this paper, we propose a new knowledge-based method for Persian WSD. Using a pre-trained LDA model, we retrieve the topics of each document and assign each ambiguous content word to one of the topics. For each possible sense s of a given word w, we compute the similarity between the FarsNet (the Persian WordNet) gloss of s and the words of the assigned topic of w. We then choose the sense with the highest score as the most probable one. We evaluated our method on a Persian all-words WSD dataset and show that, compared to other knowledge-based methods, we could achieve state-of-the-art performance.