Taboo W ordnet (2021)

Abstract: This paper describes the development of an online lexical resource to help detection systems regulate and curb the use of offensive words online. With the growing prevalence of social media platforms, many conversations are now conducted on- line. The increase of online conversations for leisure, work and socializing has led to an increase in harassment. In particular, we create a specialized sense-based vocabulary of Japanese offensive words for the Open Multilingual Wordnet. This vocabulary expands on an existing list of Japanese offen- sive words and provides categorization and proper linking to synsets within the multilingual wordnet. This paper then discusses the evaluation of the vocabulary as a resource for representing and classifying offensive words and as a possible resource for offensive word use detection in social media.

## introduction the aim of this paper is to create a sense-based lexicon of offensive and potentially inappropriate terms linked to the open multilingual wordnet @xcite . as well as adding new terms, we will categorize existing terms. the categorization is designed to be useful for both human and machine users. we distinguish between offensive terms, where the word itself has a negative connotation, and inappropriate terms, which may fine in some contexts, but not in others. real-life communication and socializing are rapidly being replaced by their online counterparts due to the overwhelming popularity and exponential growth of the use of social media platforms. social media platforms allow people to express their opinions and feelings on various topics, including social, cultural and political issues, mirroring tensions that are relevant in real-life conversations. while social media connects people instantly on a global scale, it also enables a wide-reaching and viral dissemination of harassing messages filled with inappropriate words. with more online conversations, the use of inappropriate words to express hostility and harass others also increases accordingly, and this is further amplified by the globalization of the internet where inappropriate words in different languages and cultures are utilized and manipulated to attack and offend. anonymity on social media platforms enables people to be crueler and less restrained by social conventions when they use inappropriate words in these conversations. as such, the development of new linguistic resources and computational techniques for the detection, analysis and categorization of large amounts of inappropriate words online becomes increasingly important. machine learning from annotated corpora is a very successful approach but does not provide a general solution that can be used across domains. our goal is to make multilingual online lexicons of inappropriate word meanings that can then be utilized in hate speech detection systems. due to an inadequate representation and classification of inappropriate words in physical dictionaries, these online resources and systems are essential in empowering the regulation and moderation of the use of inappropriate words online. additionally, online lexicons can be updated and edited at any time while being easily shared across the world. this paper presents the development of a lexicon of japanese inappropriate words that will be added to the open multilingual wordnet (omw) with links to existing synsets and the creation of new synsets to define new inappropriate words. these words in the omw can then be used as an online lexical resource to build awareness while analyzing and identifying inappropriate words in a multilin-gual context. ## background while there is much relevant work on the detection of offensive language @xcite foot_0 lexicons of abusive words receive little attention in literature, especially for lexicons in languages other than english. lexicons of abusive words are often manually compiled and processed specifically for a task and are not reusable in other contexts or tasks and are rarely updated once the task it was created for has been completed. one exception is hurtlex @xcite . it was created as a multilingual lexicon that is reusable and not task-specific or limited by context of its development. the creators of hurtlex expanded on the lexicon of le parole per ferire "words that hurt" @xcite and linked the words to lexical resources such as multiwordnet and ba-belnet, while translating hurtlex into a multilingual lexicon with a combination of semi-automatic translation and expert annotation. unfortunately, the hurtlex release foot_1 does not include the links to the wordnets, only lists of words. it has been updated twice, with versions 1.0, 1.1 and 1.2. there are certain criteria that a lexicon needs to fulfil in order to be effective in classifying and representing inappropriate words, let alone function as a resource for hate speech detection. the lexicon needs to be accessible, tractable, comprehensive and relevant. while hurtlex is both tractable and accessible, it cannot be considered as truly comprehensive as much of the lexicon was translated from the original french resource. this may result in a loss of semantics and nuance, especially with regards to euphemisms and cultural expressions and has no way of adding culturally specific terms. we discuss hurtlex more in the evaluation. vulgar words in dictionaries for humans are normally marked with a usage note. the naming of the note varies considerably from dictionary to dictionary, and even from edition to edition of the same dictionary @xcite . typically dictionaries have two or three levels, a sample is shown in the definitions for the terms for the american heritage dictionary (ahd) are shown below (cited in @xcite . note that fewer than ten words are in the classes ⟪obscene⟫ or ⟪usually considered vulgar⟫. vulgar the label ⟪vulgar⟫ warns of social taboos attached to a word; the label may appear alone or in combination as ⟪vulgar slang⟫. obscene a term that is considered to violate accepted standards of decency is labeled ⟪ob-scene⟫ offensive this label is reserved for terms such as racial slurs that are not only insulting and derogatory, but a discredit to the user as well. the lack of consistency in terminology, definitions of the terminology and which word is in which class show the inherent subjectivity of the decisions. interestingly, generally insulting words, such as idiot or slacker, are not normally marked in the lexicons: the only indication that a word denigrates its referent is through understanding the definition. for work on cyberbullying, such words are perhaps even more important than vulgar words. ## resources to extend the wordnets, we looked at a couple of resources. ## evaluation we used a curated list to test the coverage of our enhanced wordnet. it is the list of 1,285 offensive words from the wordnik online english dictionary. 9 note that, while we had originally worked on adding japanese senses, we are using the multilingual wordnet links to produce english data here. we compare three versions of the wordnet: original which just has those entries marked as ⟪disparagement⟫, ⟪ethnic slur⟫ or ⟪obscenity⟫ in the original pwn 3.0; offensive which has those marked as such in the taboo wordnet; and extended, which also includes everything from the domains of ⟨excrement⟩, ⟨sexuality⟩ and ⟨lgbtq+⟩. we also compared the results to the hurlex (1.2), using both the conservative and inclusive lists. the resources are summarized in the comparison in an analysis of errors shows three main causes for the lack of cover. the first is that wordnik's entries are not necessarily lemmatized in the same way as wordnet: beating your meat rather than beat one's meat "masturbate", respectively. the second is that many existing synsets do not have all the possible variants, especially colloquial entries: e.g. nut sack for "scrotum". finally, there is still a long tail of new 9 https://www.wordnik.com/lists/offensive , we removed a couple of non-offensive entries. lexicon in wordnik singular wordnet original 18 18 wordnet offensive 82 86 wordnet extended 163 172 hurtlex conservative 98 95 hurtlex inclusive 139 137 ## discussion and future work the taboo wordnet is based on synsets, not words. this is important as words such as し ゃ く は ち which could either mean しゃくはち shakuhachi "a japanese and ancient chinese longitudinal, endblown bamboo-flute" or しゃくはち shakuhachi "fellatio or oral stimulation of the penis". a word based lexicon such as hurtlex will confuse such uses. in future work, we intend to: as the taboo wordnet is an online resource, it can be updated frequently, and this allows for the taboo wordnet to reflect the ever changing status of words and expressions. such changes in status may be due to reclamation of offensive words for ingroup social pride or due to new contexts that have emerged in recent years causing some neutral words to take on a new meaning. we welcome contributions. ## conclusion the main contribution of this paper is the categorization of offensive and potentially inappropriate synsets. we have also added many japanese for these words. through the collaborative interlingual index, they can be used for future categorization and analysis of words in other languages. these additions will be made available in the omw as a resource for future studies on inappropriate words and may function as a sense-tagging tool for these words as well.