Multi-word Expressions for Abusive Speech Detection in S erbian (2020)

Abstract: This paper presents our work on the refinement and improvement of the Serbian language part of Hurtlex, a multilingual lexicon of words to hurt. We pay special attention to adding Multi-word expressions that can be seen as abusive, as such lexical entries are very important in obtaining good results in a plethora of abusive language detection tasks. We use Serbian morphological dictionaries as a basis for data cleaning and MWE dictionary creation. A connection to other lexical and semantic resources in Serbian is outlined and building of abusive language detection systems based on that connection is foreseen.

## introduction this paper presents initial results in an on-going collaboration between university of passau and university of belgrade aimed at improving the lexical resources that will aid abusive speech detection in serbian. discriminatory messages and exhortation to violence are related to offensive and hate speech, which has been gaining more attention due to the extensive use of online media and the internet in general. the concept of abusive speech, as an umbrella term for phenomena such as offensive and hate speech, its content and forms of expression are analysed, trying to define its vocabulary, collocations, colloquial expressions, and context. starting from the definition of hate speech as 'any communication that disparages a person or a group on the basis of some characteristic such as race, color, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic' @xcite it is clear that hate speech is a complex social and linguistic phenomenon. abusive language and its detection have been gaining more attention recently. @xcite define abusive language as 'hurtful language that a speaker uses to insult or offend another individual or a group of individuals based on their personal qualities, appearance, social status, opinions, statements, or actions. this might include hate speech, derogatory language, profanity, toxic comments, racist and sexist statements.' computational processing of such language requires usage of finely-tuned, task specific language tools and resources, especially for morphologically rich and lowresource languages such as serbian. in the process of building a conceptual framework of abusive language, special attention is paid to multi-word expressions (mwes) which allow for better and more precise detection of this linguistic phenomenon. the development of the mwe lexicon also helps in reducing ambiguity. the lexical resource, consisting of words that could be used as a trigger for recognition of abusive language is built, with an idea that the serbian system for recognition and normalization of abusive expressions will also take into consideration phrases and figurative speech as an indicator. both explicit and implicit abusive language (hateful and offensive messages that are not apparent at first glance) will be analysed. the remainder of the paper is structured as follows. related work is given in section 2 with a short overview of approaches for developing this type of resources in subsection 2.1. one of the existing, publicly available resources, hurtlex, that we base our work on, is introduced in subsection 2.2. the analysis and improvement of hurtlex for serbian is given in subsection 3.1. overview of acquiring, describing and classifying the additional dataset is given in section 4.1. building of the morphological dictionary and its application in graphs is given in subsection 4.2. in conclusion, our main research questions related to whether mwes include trigger words and what is their role, and are the abusive mwes mainly compositional or not, are presented at the end of the paper. ## related work the use of offensive and hateful language has been a concern since the early days of the internet. it has been estimated that the number of mwes in the lexicon of a native speaker has the same order of magnitude as the number of single words @xcite . consequently, their detection is of great importance for abusive language identification. in our abusive language detection system, we are giving the same relevance to mwes as to the individual words. without mwe identification, an expression could be marked as not abusive since it does not contain abusive words e.g. zapržiti nekome čorbu (eng. to spice up someone's stew) with figurative meaning of meddling with someone's life in a negative way, deliberately making things difficult. the majority of current hate speech, offensive and abusive language detection systems in social media are based on lexicons or blacklists @xcite @xcite . the advantage of this approach relates to a vast number of swear words and offenses that can be detected solely using lexicons. the disadvantage of using lexicons is that swear words are used in everyday speech often without offensive intent, therefore their detection may lead to false-positive results. another disadvantage of lexicons is due to the necessity of their maintenance since they evolve with natural language changes. while pedersen @xcite reported high accuracy of hate speech detection when using a lexicon only, lexicons are not sufficient as a resource for hate speech detection. they could be used as a baseline for comparison with more advanced methods since subtle hateful messages @xcite , and language nuance cannot be detected accurately using this method. in addition, some insults which might be unacceptable to one group might be acceptable to another, thus taking context into account is very important @xcite . several authors reported using a multilingual online lexicon of hate speech available at hatebase.org in their research. @xcite @xcite . @xcite built a lexicon of abusive words using the subjectivity lexicon of therese wilson that is in essence a sentiment lexicon. they took words with negative polarity as a baseline for creating a basic lexicon of 551 words, which was further enriched via machine learning into a lexicon of 2898 abusive words. several authors used the wiegand lexicon as a blacklist in their hate speech and abusive language detection systems @xcite @xcite . as noted by @xcite , lexicons that contain different part of speech give better results than those containing just nouns, therefore we employed this approach in building our first abusive words lexicon. an approach for racial, national, and religious hate speech detection adopted by @xcite was based solely on the usage of lexicon and rules. they used semantics and subjectivity features -polarity, intensity, and subjectivity level of words, using the domain corpus of hateful content and subjectivity lexicon of therese wilson in combination with the sentiwordnet @xcite .for classification, they leveraged rules and achieved a result of @xmath0.783 for strongly hateful sentences on a manually annotated domain corpus. razavi et al. (2010) created one of the first systems for flame speech detection that used a 3-layer classifier, rules, and dictionary of abusive and derogatory terms. they noticed that offensive language was characterized by extreme subjectivity. hence, they used a combination of term's offensiveness and subjectivity impact to calculate and assign a weight from 1 to 5 to each entry of their dictionary. those weights were later corrected through adaptive learning on the training data set resulting in the insulting or abusive language dictionary (iald). the dictionary contains terms such as "chew somebody's ass out" that become a search template by replacing somebody with * or by adding suffixes or prefixes to verbs or nouns (e.g. suffix -ing for verbs and -es to nouns). they achieved an accuracy of 96,78% in 10-fold cross-validation on the binary classifier. ## conclusion in this paper we presented initial results on the analysis, improvement and creation of the lexical resources that will aid abusive speech detection tasks in serbian, with a special focus given to mwes, but there is still much to be done. options of using a hybrid approach that would merge a dictionary with machine-learning will be explored. finally, a user-friendly interface that will enable the usage of these resources on the web is under development. we plan to use our lexicon of abusive speech to build a corpus of abusive content similar to @xcite who firstly created an offensive word lexicon and then collected twitter messages that contain at least one word from the lexicon. as authors noted, presence of a word in a tweet is just an indication of its offensiveness, thus subsequent manual annotation is mandatory to assure correct classification of tweets. in the next phases of the abusive words lexicon development, we plan to use: lists of slurs, abusive expressions, and courses built by conducting surveys and crowdsourcing @xcite , slang and dictionaries of synonyms, translation of existing lexicons in other languages, sentiment lexicon for serbian language @xcite , synsets from the serbian wordnet @xcite . we plan to use the lexicon for building a corpus of abusive content in social networks in serbian as well as a classifier using rules and existing resources for serbian language @xcite . in addition, we plan to include the context rules and intensifiers following the approach presented in @xcite about the mwes sentiment lexicon for spanish. additional attention will be given to the extension of the vocabulary with expressions that are not present in any existing lexicons, but evidenced in corpus as having offensive usage. the recognition of the different usages, that can be both offensive and non-offensive will be marked. the additional information about context or sense embeddings that will be useful for distinguishing between the two usages, could be added in the lexicon.