Language Resources and Evaluation最新文献_第6页

Large scale annotated dataset for code-mix abusive short noisy text 大规模注释数据集，用于编码混合滥用短篇高噪声文本

IF 2.7 3区计算机科学

Language Resources and Evaluation Pub Date : 2024-01-25 DOI: 10.1007/s10579-023-09707-7

{"title":"Large scale annotated dataset for code-mix abusive short noisy text","authors":"","doi":"10.1007/s10579-023-09707-7","DOIUrl":"https://doi.org/10.1007/s10579-023-09707-7","url":null,"abstract":"<h3>Abstract</h3> With globalization and cultural exchange around the globe, most of the population gained knowledge of at least two languages. The bilingual user base on the Social Media Platform (SMP) has significantly contributed to the popularity of code-mixing. However, apart from multiple vital uses, SMP also suffer with abusive text content. Identifying abusive instances for a single language is a challenging task, and even more challenging for code-mix. The abusive posts detection problem is more complicated than it seems due to its unseemly, noisy data and uncertain context. To analyze these contents, the research community needs an appropriate dataset. A small dataset is not a suitable sample for the research work. In this paper, we have analyzed the dimensions of Devanagari-Roman code-mix in short noisy text. We have also discussed the challenges of abusive instances. We have proposed a cost-effective methodology with 20.38% relevancy score to collect and annotate the code-mix abusive text instances. Our dataset is eight times to the related state-of-the-art dataset. Our dataset ensures the balance with 55.81% instances in the abusive class and 44.19% in the non-abusive class. We have also conducted experiments to verify the usefulness of the dataset. We have performed experiments with traditional machine learning techniques, traditional neural network architecture, recurrent neural network architectures, and pre-trained Large Language Model (LLM). From our experiments, we have observed the suitability of the dataset for further scientific work.","PeriodicalId":49927,"journal":{"name":"Language Resources and Evaluation","volume":"164 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139560843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A flexible tool for a qualia-enriched FrameNet: the FrameNet Brasil WebTool 一个用于丰富质点的框架网的灵活工具：巴西框架网网络工具

IF 2.7 3区计算机科学

Language Resources and Evaluation Pub Date : 2024-01-22 DOI: 10.1007/s10579-023-09714-8

Tiago Timponi Torrent, Ely Edison da Silva Matos, Alexandre Diniz da Costa, Maucha Andrade Gamonal, Simone Peron-Corrêa, Vanessa Maria Ramos Lopes Paiva

引用次数: 0

NewsCom-TOX: a corpus of comments on news articles annotated for toxicity in Spanish NewsCom-TOX：西班牙语新闻文章评论注释语料库

IF 2.7 3区计算机科学

Language Resources and Evaluation Pub Date : 2024-01-17 DOI: 10.1007/s10579-023-09711-x

Mariona Taulé, Montserrat Nofre, Víctor Bargiela, Xavier Bonet

{"title":"NewsCom-TOX: a corpus of comments on news articles annotated for toxicity in Spanish","authors":"Mariona Taulé, Montserrat Nofre, Víctor Bargiela, Xavier Bonet","doi":"10.1007/s10579-023-09711-x","DOIUrl":"https://doi.org/10.1007/s10579-023-09711-x","url":null,"abstract":"In this article, we present the NewsCom-TOX corpus, a new corpus manually annotated for toxicity in Spanish. NewsCom-TOX consists of 4359 comments in Spanish posted in response to 21 news articles on social media related to immigration, in order to analyse and identify messages with racial and xenophobic content. This corpus is multi-level annotated with different binary linguistic categories -stance, target, stereotype, sarcasm, mockery, insult, improper language, aggressiveness and intolerance- taking into account not only the information conveyed in each comment, but also the whole discourse thread in which the comment occurs, as well as the information conveyed in the news article, including their images. These categories allow us to identify the presence of toxicity and its intensity, that is, the level of toxicity of each comment. All this information is available for research purposes upon request. Here we describe the NewsCom-TOX corpus, the annotation tagset used, the criteria applied and the annotation process carried out, including the inter-annotator agreement tests conducted. A quantitative analysis of the results obtained is also provided. NewsCom-TOX is a linguistic resource that will be valuable for both linguistic and computational research in Spanish in NLP tasks for the detection of toxic information.","PeriodicalId":49927,"journal":{"name":"Language Resources and Evaluation","volume":"14 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139497797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toxic comment classification and rationale extraction in code-mixed text leveraging co-attentive multi-task learning 利用共同关注多任务学习在代码混合文本中进行有毒评论分类和理由提取

IF 2.7 3区计算机科学

Language Resources and Evaluation Pub Date : 2024-01-13 DOI: 10.1007/s10579-023-09708-6

Kiran Babu Nelatoori, Hima Bindu Kommanti

{"title":"Toxic comment classification and rationale extraction in code-mixed text leveraging co-attentive multi-task learning","authors":"Kiran Babu Nelatoori, Hima Bindu Kommanti","doi":"10.1007/s10579-023-09708-6","DOIUrl":"https://doi.org/10.1007/s10579-023-09708-6","url":null,"abstract":"Detecting toxic comments and rationale for the offensiveness of a social media post promotes moderation of social media content. For this purpose, we propose a Co-Attentive Multi-task Learning (CA-MTL) model through transfer learning for low-resource Hindi-English (commonly known as Hinglish) toxic texts. Together, the cooperative tasks of rationale/span detection and toxic comment classification create a strong multi-task learning objective. A task collaboration module is designed to leverage the bi-directional attention between the classification and span prediction tasks. The combined loss function of the model is constructed using the individual loss functions of these two tasks. Although an English toxic span detection dataset exists, one for Hinglish code-mixed text does not exist as of today. Hence, we developed a dataset with toxic span annotations for Hinglish code-mixed text. The proposed CA-MTL model is compared against single-task and multi-task learning models that lack the co-attention mechanism, using multilingual and Hinglish BERT variants. The F1 scores of the proposed CA-MTL model with HingRoBERTa encoder for both tasks are significantly higher than the baseline models. Caution: This paper may contain words disturbing to some readers.","PeriodicalId":49927,"journal":{"name":"Language Resources and Evaluation","volume":"27 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139460881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-layered semantic annotation and the formalisation of annotation schemas for the investigation of modality in a Latin corpus 为研究拉丁语语料库中的模态而进行多层语义标注和标注模式正规化

IF 2.7 3区计算机科学

Language Resources and Evaluation Pub Date : 2024-01-06 DOI: 10.1007/s10579-023-09706-8

引用次数: 0

AC-IQuAD: Automatically Constructed Indonesian Question Answering Dataset by Leveraging Wikidata AC-IQuAD：利用维基数据自动构建印尼语问题解答数据集

IF 2.7 3区计算机科学

Language Resources and Evaluation Pub Date : 2024-01-03 DOI: 10.1007/s10579-023-09702-y

Kerenza Doxolodeo, Adila Alfa Krisnadhi

引用次数: 0

KurdiSent: a corpus for kurdish sentiment analysis KurdiSent：库尔德人情感分析语料库

IF 2.7 3区计算机科学

Language Resources and Evaluation Pub Date : 2024-01-02 DOI: 10.1007/s10579-023-09716-6

Soran Badawi, Arefeh Kazemi, Vali Rezaie

{"title":"KurdiSent: a corpus for kurdish sentiment analysis","authors":"Soran Badawi, Arefeh Kazemi, Vali Rezaie","doi":"10.1007/s10579-023-09716-6","DOIUrl":"https://doi.org/10.1007/s10579-023-09716-6","url":null,"abstract":"Language is essential for communication and the expression of feelings and sentiments. As technology advances, language has become increasingly ubiquitous in our lives. One of the most critical research areas in natural language processing (NLP) is sentiment analysis, which aims to identify and extract opinions and attitudes from text. Sentiment analysis is particularly useful for understanding public opinion on products, services, and topics of interest. While sentiment analysis systems are well-developed for English, this differs for other languages, such as Kurdish. This is because less-resourced languages have fewer NLP resources, including annotated datasets. To bridge this gap, this paper introduces KurdiSent, the first manually annotated dataset for Kurdish sentiment analysis. KurdiSent consists of over 12,000 instances labeled as positive, negative, or neutral. The corpus covers the Sorani dialect of Kurdish, the most widely spoken dialect. To ensure the quality of KurdiSent, the dataset was trained on machine learning and deep learning classifiers. The experimental results indicated that XLM-R outperformed all machine learning and deep learning classifiers, with an accuracy of 85%, compared to 81% for the best machine learning classifier. KurdiSent is a valuable resource for the NLP community, as it will enable researchers to develop and improve sentiment analysis systems for Kurdish. The corpus will facilitate a better understanding of public opinion in Kurdish-speaking communities.","PeriodicalId":49927,"journal":{"name":"Language Resources and Evaluation","volume":"21 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139083544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Syntactic annotation for Portuguese corpora: standards, parsers, and search interfaces 葡萄牙语语法注释：标准、解析器和搜索界面

IF 2.7 3区计算机科学

Language Resources and Evaluation Pub Date : 2023-12-26 DOI: 10.1007/s10579-023-09699-4

Pablo Faria, Charlotte Galves, Catarina Magro

{"title":"Syntactic annotation for Portuguese corpora: standards, parsers, and search interfaces","authors":"Pablo Faria, Charlotte Galves, Catarina Magro","doi":"10.1007/s10579-023-09699-4","DOIUrl":"https://doi.org/10.1007/s10579-023-09699-4","url":null,"abstract":"In the last two decades, four Portuguese syntactically annotated corpora were built along the lines initially defined for the Penn Parsed Historical Corpora (Santorini, 2016). They cover the old, the middle, the classical and the modern periods of European Portuguese, as well as the nineteenth and twentieth century Brazilian Portuguese, and include different textual genres and oral discourse excerpts. Together they provide a fundamental resource for the study of variation and change in Portuguese. In the last years, an effort was made to maximally unify the annotation scheme applied to those corpora, in such a way that the searches done on one corpus could be done in exactly the same manner on the others. This effort resulted in the Portuguese Syntactic Annotation Manual (Magro & Galves, 2019). In this paper, we present the syntactic annotation for the Portuguese Corpora. We describe the functioning of ParsPort, a rule-based parser which makes use of the revision mode of the query language Corpus Search (Randall, 2005–2015). We argue that ParsPort is more efficient to our annotation efforts than the probabilistic parser developed by Bikel (2004), previously used for the syntactic annotation of the Portuguese Corpora. Finally we mention recent advances towards more user-friendly tools for syntactic searches.","PeriodicalId":49927,"journal":{"name":"Language Resources and Evaluation","volume":"5 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139056467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linguistic annotation of Byzantine book epigrams 拜占庭书信体的语言注释

IF 2.7 3区计算机科学

Language Resources and Evaluation Pub Date : 2023-12-13 DOI: 10.1007/s10579-023-09703-x

Colin Swaelens, Ilse De Vos, Els Lefever

引用次数: 0

Democratizing neural machine translation with OPUS-MT 利用 OPUS-MT 实现神经机器翻译的民主化

IF 2.7 3区计算机科学

Language Resources and Evaluation Pub Date : 2023-12-13 DOI: 10.1007/s10579-023-09704-w

Jörg Tiedemann, Mikko Aulamo, Daria Bakshandaeva, Michele Boggia, Stig-Arne Grönroos, Tommi Nieminen, Alessandro Raganato, Yves Scherrer, Raúl Vázquez, Sami Virpioja

引用次数: 0