  Wordnets are indispensable tools for various natural language processing
applications. Unfortunately, wordnets get outdated, and producing or updating
wordnets can be slow and costly in terms of time and resources. This problem
intensifies for low-resource languages. This study proposes a method for word
sense induction and synset induction using only two linguistic resources,
namely, an unlabeled corpus and a sentence embeddings-based language model. The
resulting sense inventory and synonym sets can be used in automatically
creating a wordnet. We applied this method on a corpus of Filipino text. The
sense inventory and synsets were evaluated by matching them with the sense
inventory of the machine translated Princeton WordNet, as well as comparing the
synsets to the Filipino WordNet. This study empirically shows that the 30% of
the induced word senses are valid and 40% of the induced synsets are valid in
which 20% are novel synsets.
