Building Synsets for Indonesian WordNet with Monolingual Lexical Resources

Abstract

This paper presents an approach to build synsets for Indonesian WordNet semi-automatically using monolingual lexical resources available freely in Bahasa Indonesia. Monolingual lexical resources refer to Kamus Besar Bahasa Indoensia or KBBI (monolingual dictionary of Bahasa Indonesia) and Tesaurus Bahasa Indonesia (Indonesian thesaurus). We assume that monolingual resources will play an important role in synsets building, because it will provide more accurate senses specifically for Bahasa. Besides, resources that have been used are produced by Bahasa Indonesia Language Center, which is a government institution that manages Bahasa Indonesia development. However, the synsets retrieved will be considered as a prototype version of Indonesian WordNet synsets.

Query Rewriting with Thesaurus-Based for Handling Semantic Heterogeneity in Database Integration

“…The selected thesaurus is WordNet. The words in Wordnet are organized into a set of a synonym (synset) [11]. Each set closely related to other synset based on semantic relationships such as synonym, hyponym, hypernym, and antonym.…”

Automatic text summarization based on semantic analysis approach for documents in Indonesian language

“…The common ontology knowledge is WordNet [11], [12] as a lexical ontology word resource. Therefore, our research used WordNet for Indonesian language [13], [14] as the lexical word resources.…”

Automatic Wordnet Development for Low-Resource Languages using Cross-Lingual WSD

“…On the other hand, methods that follow the expansion approach usually adopt WordNet structure and find the correct translation of the associated words with the WordNet synsets in the target language. In this process, multilingual resources such as comparable corpora (Kaji & Watanabe, 2006), parallel corpora (Oliver & Climent, 2012;Kazakov & Shahid, 2009;Fišer, 2009;Diab, 2004), thesaurus (Gunawan & Saputra, 2010), machine translators (Saveski & Trajkovski, 2010) and multiple bi-lingual machine readable dictionaries (Atserias, Climent, Farreres, Rigau, & Rodríguez, 2000;Patanakul & Charnyote, 2005;Bond, Isahara, Kanzaki, & Uchimoto, 2008;Lam, Al Tarouti, & Kalita, 2014) are used, which causes a bottleneck for low-resource languages.…”