Identifying and Exploiting Definitions in W ordnet B ahasa (2016)

Abstract: This paper describes our attempts to add Indonesian definitions to synsets in the Wordnet Bahasa (Nurril Hirfana Mohamed Noor et al., 2011; Bond et al., 2014), to extract semantic relations between lemmas and definitions for nouns and verbs, such as synonym, hyponym, hypernym and instance hypernym, and to generally improve Wordnet. The original, somewhat noisy, definitions for Indonesian came from the Asian Wordnet project (Riza et al., 2010). The basic method of extracting the relations is based on Bond et al. (2004). Before the relations can be extracted, the definitions were cleaned up and tokenized. We found that the definitions cannot be completely cleaned up because of many misspellings and bad translations. However, we could identify four semantic relations in 57.10% of noun and verb definitions. For the remaining 42.90%, we propose to add 149 new Indonesian lemmas and make some improvements to Wordnet Bahasa and Wordnet in general.

## introduction a lexical database with comprehensive data about words, definitions, and examples is very useful in language research. in princeton wordnet, nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive synonyms (synsets) which are interlinked through a number of semantic relations @xcite . since its creation, many other wordnets in different languages have been built based on princeton wordnet (pwn) @xcite . one of them, wordnet bahasa, is built as a lexical database of the malay language. at present, it consists of two language variants: indonesian and standard malay. it combines data from several lexical resources: the french-english-malay dictionary (fem), the kamus melayu-inggeris (kami), and wordnets for english, french and chinese @xcite . we added indonesian definitions from the asian wordnet project @xcite to wordnet bahasa. to the best of our knowledge, the asian wordnet project is the only project that translated the english definitions of some synsets in pwn into indonesian. however, the definitions were crowd sourced and had little quality control so not all of 14,190 definitions could be directly transferred. many of the definitions had problems and needed to be cleaned up. the definitions for nouns and verbs which had been cleaned up were exploited to extract relations, such as synonym, hyponym, hypernym and instance hypernym, between lemmas and definitions. the method of extracting these relations was done in @xcite to build an ontology. we used python (3.4, python software foundation) and the natural language toolkit (nltk) @xcite to process the data. this paper is organized as follows: section 2 describes the process of cleaning up the definitions, section 3 explains the process of extracting hypernyms and other relations from the definitions. section 4 presents the results and discussion and section 5 concludes. ## cleaning up the definitions as mentioned in section 1 above, the definitions we had available were not clean. many infelicities were found, such as misspellings, definitions using abbreviations, typos, synsets having more than one similar definitions, definitions written in english, improper use of hyphens, and lemmas written as the first word in the definitions. each error is illustrated in the following subsections. ## results and discussion the definition file which originally has 14,190 lines of definitions was cleaned up and 1,522 definitions (10.7%) were deleted. the remaining 12,668 definitions consist of 10,549 definitions for nouns, 1,663 definitions for adjectives, 409 definitions for verbs, and 47 definitions for adverbs. although these definitions are considered quite clean, they may still contain small errors as mentioned in section 2.1. since adjectives and adverbs do not have relations such as hypernym in wordnet, we only examined nouns and verbs. out of 10,958 definitions for nouns and verbs, we could extract four relations from 6,257 definitions (57.10%) as shown in most of the relations we extracted (95.89%) are hypernym and instance hypernym. the remaining are synonym and hyponym as shown in out of the 4,701 definitions for which we could not find the relations, most of them (83.88%) have hypernyms which are different from the first word in the definitions. we found four patterns for this problem (see for example, synset 13251154-n has istilah "terms" and synset 07603411-n has singkatan "abbreviation" as the first word in the definition. to get the real genus term requires more parsing. the second problem we found is that the first word in 747 definitions (15.89%) is not in wordnet. in this case, we checked whether the word is in the indonesian dictionary (kbbi) or not as mentioned in the previous section. we found 252 definitions having 149 unique words (the heads) which are in kbbi but not in wordnet. some of them are compounds as in synset 07865105-n with the definition hot dog dengan daging sapi diberi cabai bubuk "a hotdog with chili con carne on it". we did not distinguish compounds and thus, failed to extract hot dog as the head. the word hot does exist in kbbi as an adjective meaning 'sexually excited or exciting'. the remaining 495 definitions have 235 unique words which are not in kbbi. we examined three patterns for this: we found 11 lemmas have no explicit semantic relations with the definitions. they are all verbs: 01773734-v 'grudge', @xcite prosecute', and 02673965-v 'stand out'. ## summary and future work we have presented the process of cleaning up the definitions and extracting relations from the definitions. while doing the relation extraction, we spotted errors such as incompleteness and incorrectness in the definitions which we could not detect only by cleaning up the definitions. the reason why there are errors is probably because of little quality control in the translation process. in addition, we found things to be improved in wordnet bahasa and wordnet in general. based on our findings, we propose to: for example, for synset 04658942-n 'inhospitableness' memiliki sifat tidak ramah "having an unfriendly and inhospitable disposition" and synset 04657876-n 'unfriendliness' "an unfriendly disposition"