TREM o L o-Tweets: A Multi-Label Corpus of F rench Tweets for Language Register Characterization (2021)

Abstract: The casual, neutral, and formal language registers are highly perceptible in discourse productions. However, they are still poorly studied in Natural Language Processing (NLP), especially outside English, and for new textual types like tweets. To stimulate research, this paper introduces a large corpus of 228,505 French tweets (6M words) annotated in language registers. Labels are provided by a multi-label CamemBERT classifier trained and checked on a manually annotated subset of the corpus, while the tweets are selected to avoid undesired biases. Based on the corpus, an initial analysis of linguistic traits from either human annotators or automatic extractions is provided to describe the corpus and pave the way for various NLP tasks. The corpus, annotation guide and classifier are available on http://tremolo.irisa.fr .

## introduction language registers are of particular interest in (socio-)linguistics because (1) they are a highly perceptible characteristics of discourse productions ; (2) they represent a significant source of information about the writer/speaker, the relationship between interlocutors, or other elements of the communication context ; (3) they are a concept known to all (advantage when running perceptual tests). among the possible perceptions of this phenomenon, the partitioning into casual, neutral, and formal registers is probably the most used as it is found in many situations of everyday life. while corpora like gyafc-where these variations are referred to as "formality level"have recently popularized the domain @xcite , it is still poorly studied overall in natural language processing (nlp), especially outside english. moreover, current work largely focuses on textual types for which registers are al-ready known from the linguistic literature foot_0 whereas many new types, with their peculiarities, arise from the computer-mediated communications (cmcs) (e.g., sms, tweets...). therefore, the analysis of cmc corpora in terms of language registers is a challenge both in terms of descriptive linguistics and applications in nlp. as part of the tremolo project focusing on language registers foot_1 , this paper tries to go beyond these limits and presents the corpus tremolo-tweets, gathering 228,505 tweets (6m words), in french, with multi-label annotations among the casual, neutral and formal registers. the annotations come from a camembert @xcite model fine-tuned on a manually annotated subset (a.k.a. seed) of the whole corpus. after a state of the art in section 2, the corpus creation is presented in section 3. then, section 4 provides first linguistic conclusions derived from statistics on manually and automatically-derived linguistic traits. finally, possible tasks opened by the proposed corpus are listed in the conclusion. ## background and motivation notion of registers. in sociolinguistics, the notion of language registers refers to the linguistic varieties associated with particular communication situations @xcite . a key idea is that a language register can be characterized by specific patterns @xcite . while the use of "level", "style" or "genre" co-exist @xcite @xcite , the term "register" tends to prevail @xcite @xcite . based on these points, we use the term "register" defined as a variation of linguistic forms, at different levels of analysis of the language, with respect to a given standard. this standard corresponds to the intersection of an "objective norm" (the grammatical rules) and a "subjective norm" (the rules of actual usage) @xcite . following this definition, a text is considered as formal when it completely conforms to the objective and subjective norms, neutral when it partially conforms to both, and casual when the objective norm is not followed. ## corpus creation the corpus tremolo-tweets is drawn from tweets collected in such a way as to cover the targeted spectrum of language registers while minimizing some unwanted biases. after various filterings and cleanings, a subset of these tweets was manually labeled. from a portion of these labeled tweets (training set), a camembert classifier was fine-tuned to generalize the labels to the whole corpus. the result was validated on another part of the manually annotated tweets (test set). this section details the collection of the tweets, the labeling process, and the experimental validation. ## linguistic analysis this section presents a first linguistic analysis of the corpus tremolo-tweets in terms of language registers. after presenting the underlying linguistic descriptors, this section reports which of these descriptors have mostly been selected by the annotators, and how they appear on the whole corpus using systematic automatic extractions. ## conclusion in this paper, we presented the corpus tremolo-tweets which gathers 228,505 tweets labeled in casual, neutral and formal registers. for this purpose, a seed was manually annotated with multiple labels, following an annotation guide derived from a linguistic analysis of the corpus. using a camembert model and data augmentation, the whole corpus is entirely labeled with an experimentally demonstrated high reliability. furthermore, statistics on linguistic descriptors are reported to demonstrate the richness of the corpus. the labels, linguistic descriptors, and the large size of the corpus pave the way to future tasks: