--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ru license: - apache-2.0 multilinguality: - monolingual pretty_name: The Corpus for the analysis of author profiling in Russian-language texts. size_categories: - 10K list: """ Read list of objects from a JSON lines file. """ data = [] with open(input_path, 'r', encoding='utf-8') as f: for line in f: data.append(json.loads(line.rstrip('\n|\r'))) print('Loaded {} records from {}/n'.format(len(data), input_path)) return data path_to_file = "./data/train.jsonl" data = load_jsonl(path_to_file) ``` or you can use HuggingFace style: ``` from datasets import load_dataset train_df = load_dataset('sagteam/author_profiling', split='train') valid_df = load_dataset('sagteam/author_profiling', split='validation') test_df = load_dataset('sagteam/author_profiling', split='test') ``` #### Here are some statistics: 1. For Train file: - No. of documents -- 9564; - No. of unique texts -- 9553; - Text length in characters -- min: 197, max: 2984, mean: 500.5; - No. of documents written -- by men: 4704, by women: 4860; - No. of unique authors -- 2344; men: 1172, women: 1172; - Age of the authors -- min: 13, max: 80, mean: 31.2; - No. of documents by age group -- 0-19: 813, 20-29: 4188, 30-39: 2697, 40-49: 1194, 50+: 672; - No. of documents with gender imitation: 1215; without gender imitation: 2430; not applicable: 5919; - No. of documents with age imitation -- younger: 1973; older: 1973; without age imitation: 1973; not applicable: 3645; - No. of documents with style imitation: 1215; without style imitation: 2430; not applicable: 5919. 2. For Valid file: - No. of documents -- 1320; - No. of unique texts -- 1316; - Text length in characters -- min: 200, max: 2809, mean: 520.8; - No. of documents written -- by men: 633, by women: 687; - No. of unique authors -- 336; men: 168, women: 168; - Age of the authors -- min: 15, max: 79, mean: 32.2; - No. of documents by age group -- 1-19: 117, 20-29: 570, 30-39: 339, 40-49: 362, 50+: 132; - No. of documents with gender imitation: 156; without gender imitation: 312; not applicable: 852; - No. of documents with age imitation -- younger: 284; older: 284; without age imitation: 284; not applicable: 468; - No. of documents with style imitation: 156; without style imitation: 312; not applicable: 852. 3. For Test file: - No. of documents -- 2564; - No. of unique texts -- 2561; - Text length in characters -- min: 199, max: 3981, mean: 515.6; - No. of documents written -- by men: 1290, by women: 1274; - No. of unique authors -- 672; men: 336, women: 336; - Age of the authors -- min: 12, max: 67, mean: 31.8; - No. of documents by age group -- 1-19: 195, 20-29: 1131, 30-39: 683, 40-49: 351, 50+: 204; - No. of documents with gender imitation: 292; without gender imitation: 583; not applicable: 1689; - No. of documents with age imitation -- younger: 563; older: 563; without age imitation: 563; not applicable: 875; - No. of documents with style imitation: 292; without style imitation: 583; not applicable: 1689. ### Supported Tasks and Leaderboards This dataset is intended for multi-class and multi-label text classification. The baseline models currently achieve the following F1-weighted metrics scores (table): | Model name | gender | age_group | gender_imitation | age_imitation | style_imitation | no_imitation | average | | ------------------- | ------ | --------- | ---------------- | ------------- | --------------- | ------------ | ------- | | Dummy-stratified | 0.49 | 0.29 | 0.56 | 0.32 | 0.57 | 0.55 | 0.46 | | Dummy-uniform | 0.49 | 0.23 | 0.51 | 0.32 | 0.51 | 0.51 | 0.43 | | Dummy-most_frequent | 0.34 | 0.27 | 0.53 | 0.17 | 0.53 | 0.53 | 0.40 | | LinearSVC + TF-IDF | 0.67 | 0.37 | 0.62 | 0.72 | 0.71 | 0.71 | 0.63 | ### Languages The text in the dataset is in Russian. ## Dataset Structure ### Data Instances Each instance is a text in Russian with some author profiling annotations. An example for an instance from the dataset is shown below: ``` { 'id': 'crowdsource_4916', 'text': 'Ты очень симпатичный, Я давно не с кем не встречалась. Ты мне сильно понравился, ты умный интересный и удивительный, приходи ко мне в гости , у меня есть вкусное вино , и приготовлю вкусный ужин, посидим пообщаемся, узнаем друг друга поближе.', 'account_id': 'account_#1239', 'author_id': 411, 'age': 22, 'age_group': '20-29', 'gender': 'male', 'no_imitation': 'with_any_imitation', 'age_imitation': 'None', 'gender_imitation': 'with_gender_imitation', 'style_imitation': 'no_style_imitation' } ``` ### Data Fields Data Fields includes: - id -- unique identifier of the sample; - text -- authors text written by a crowdsourcing user; - author_id -- unique identifier of the user; - account_id -- unique identifier of the crowdsource account; - age -- age annotations; - age_group -- age group annotations; - no_imitation -- imitation annotations. Label codes: - 'with_any_imitation' -- there is some imitation in the text; - 'no_any_imitation' -- the text is written without any imitation - age_imitation -- age imitation annotations. Label codes: - 'younger' -- someone younger than the author is imitated in the text; - 'older' -- someone older than the author is imitated in the text; - 'no_age_imitation' -- the text is written without age imitation; - 'None' -- not supported (the text was not written for this task) - gender_imitation -- gender imitation annotations. Label codes: - 'no_gender_imitation' -- the text is written without gender imitation; - 'with_gender_imitation' -- the text is written with a gender imitation; - 'None' -- not supported (the text was not written for this task) - style_imitation -- style imitation annotations. Label codes: - 'no_style_imitation' -- the text is written without style imitation; - 'with_style_imitation' -- the text is written with a style imitation; - 'None' -- not supported (the text was not written for this task). ### Data Splits The dataset includes a set of train/valid/test splits with 9564, 1320 and 2564 texts respectively. The unique authors do not overlap between the splits. ## Dataset Creation ### Curation Rationale The formed dataset of examples consists of texts in Russian using a crowdsourcing platform. The created dataset can be used to improve the accuracy of supervised classifiers in author profiling tasks. ### Source Data #### Initial Data Collection and Normalization Data was collected from crowdsource platform. Each text was written by the author specifically for the task provided. #### Who are the source language producers? Russian-speaking Yandex.Toloka users. ### Annotations #### Annotation process We used a crowdsourcing platform to collect texts. Each respondent is asked to fill a questionnaire including their gender, age and native language. For age imitation task the respondents are to choose a topic out of a few suggested, and write three texts on it: 1) Text in their natural manner; 2) Text imitating the style of someone younger; 3) Text imitating the style of someone older. For gender and style imitation task each author wrote three texts in certain different styles: 1) Text in the authors natural style; 2) Text imitating other gender style; 3) Text in a different style but without gender imitation. The topics to choose from are the following. - An attempt to persuade some arbitrary listener to meet the respondent at their place; - A story about some memorable event/acquisition/rumour or whatever else the imaginary listener is supposed to enjoy; - A story about oneself or about someone else, aiming to please the listener and win their favour; - A description of oneself and one’s potential partner for a dating site; - An attempt to persuade an unfamiliar person to come; - A negative tour review. The task does not pass checking and is considered improper work if it contains: - Irrelevant answers to the questionnaire; - Incoherent jumble of words; - Chunks of text borrowed from somewhere else; - Texts not conforming to the above list of topics. Texts checking is performed firstly by automated search for borrowings (by an anti-plagiarism website), and then by manual review of compliance to the task. #### Who are the annotators? Russian-speaking Yandex.Toloka users. ### Personal and Sensitive Information All personal data was anonymized. Each author has been assigned an impersonal, unique identifier. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators Researchers at AI technology lab at NRC "Kurchatov Institute". See the [website](https://sagteam.ru/). ### Licensing Information Apache License 2.0. ### Citation Information If you have found our results helpful in your work, feel free to cite our publication. ``` @article{сбоев2022сравнение, title={СРАВНЕНИЕ ТОЧНОСТЕЙ МЕТОДОВ НА ОСНОВЕ ЯЗЫКОВЫХ И ГРАФОВЫХ НЕЙРОСЕТЕВЫХ МОДЕЛЕЙ ДЛЯ ОПРЕДЕЛЕНИЯ ПРИЗНАКОВ АВТОРСКОГО ПРОФИЛЯ ПО ТЕКСТАМ НА РУССКОМ ЯЗЫКЕ}, author={Сбоев, АГ and Молошников, ИА and Рыбка, РБ and Наумов, АВ and Селиванов, АА}, journal={Вестник Национального исследовательского ядерного университета МИФИ}, volume={10}, number={6}, pages={529--539}, year={2021}, publisher={Общество с ограниченной ответственностью МАИК "Наука/Интерпериодика"} } ``` ### Contributions Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset.