Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation最新文献

EmoThreat@FIRE2022: Shared Track on Emotions and Threat Detection in Urdu EmoThreat@FIRE2022:乌尔都语情绪和威胁检测共享轨道

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574327

S. Butt, Maaz Amjad, Fazlourrahman Balouchzah, Noman Ashraf, Rajesh Sharma, G. Sidorov, A. Gelbukh

{"title":"EmoThreat@FIRE2022: Shared Track on Emotions and Threat Detection in Urdu","authors":"S. Butt, Maaz Amjad, Fazlourrahman Balouchzah, Noman Ashraf, Rajesh Sharma, G. Sidorov, A. Gelbukh","doi":"10.1145/3574318.3574327","DOIUrl":"https://doi.org/10.1145/3574318.3574327","url":null,"abstract":"Many languages with a wealth of resources have been researched to solve the challenges of emotion and targeted abuse detection, i.e. threat. But when it comes to languages, such as Urdu, it is noted that there is a severe lack of both resources and approaches in terms of Urdu language processing. Therefore, this study concentrated on offering resources for Urdu by organizing a shared task called “EmoThreat: Emotions and Threat detection in Urdu\". The task offered two tasks: (i) multi-label emotion classification (Task A), and (ii) binary threat detection (Task B). Task B was a multi-class problem since it was further subdivided into the identification of threats posed by groups and individuals. This paper provides an overview of the methodology and results obtained by each of the 10 distinct teams who participated in the shared task. In addition, each group presented a detailed error analysis as part of their submission for the best model. The top-performing system in Task A received a macro-F1 score of 0.687. In contrast, subtask 1 of Task B received a score of 0.716 macro-F1 while subtask 2 of Task B obtained a 0.539 macro-F1 score.","PeriodicalId":270700,"journal":{"name":"Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124259216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis) FIRE 2022专题概述:灾害期间从微博中获取信息(IRMiDis)

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574319

Soham Poddar, Moumita Basu, Kripabandhu Ghosh, Saptarshi Ghosh

引用次数: 0

Design Considerations for a Sustainable Scholarly Big Data Service 可持续学术大数据服务的设计考虑

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574340

Jian Wu, Shaurya Rohatgi, Manoj K. Angadi, Kavya S. Puranik, C. Lee Giles

{"title":"Design Considerations for a Sustainable Scholarly Big Data Service","authors":"Jian Wu, Shaurya Rohatgi, Manoj K. Angadi, Kavya S. Puranik, C. Lee Giles","doi":"10.1145/3574318.3574340","DOIUrl":"https://doi.org/10.1145/3574318.3574340","url":null,"abstract":"The advancement of web programming techniques, such as Ajax and jQuery, and datastores, such as Apache Solr and Elasticsearch, have made it much easier to deploy small to medium scale web-based search engines. However, developing a sustainable search engine that supports scholarly big data services is still challenging often because of limited human resources and financial support. Such scenarios are typical in academic settings or small businesses. Here, we showcase how four key design decisions were made by trading-off competing factors such as performance, cost, and efficiency, when developing the Next Generation CiteSeerX (NGX), the successor of CiteSeerX, which was a pioneering digital library search engine that has been serving academic communities for more than two decades. This work extends our previous work in Wu et al. (2021) and discusses design considerations of infrastructure, web applications, indexing, and document filtering. These design considerations can be generalized to other web-based search engines with a similar scale that are deployed in small business or academic settings with limited resources.","PeriodicalId":270700,"journal":{"name":"Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130387342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Topic-Mono-BERT: A Joint Retrieval-Clustering System for Retrieving Overview Passages Topic-Mono-BERT:一个用于检索概述文章的联合检索-聚类系统

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574336

Sumanta Kashyapi, Laura Dietz

引用次数: 0

A Multilingual Dataset for Identification of Factual Claims in Indian Twitter 印度推特中事实声明识别的多语言数据集

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574348

Subhabrata Dutta, Rudra Dhar, Prantik Guha, Arpan Murmu, Dipankar Das

引用次数: 1

Classification of Waste Materials using CNN Based on Transfer Learning 基于迁移学习的CNN废弃物分类

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574345

Sujan Poudel, Prakash Poudyal

{"title":"Classification of Waste Materials using CNN Based on Transfer Learning","authors":"Sujan Poudel, Prakash Poudyal","doi":"10.1145/3574318.3574345","DOIUrl":"https://doi.org/10.1145/3574318.3574345","url":null,"abstract":"Waste Management is important for humans as well as nature for healthy life and a clean environment. The major step for effective waste management is the segregation of waste according to its types. The advancement of technology such as hardware and artificial intelligence is used for the segregation of waste. There are several machine learning and deep learning algorithms available for image classification. Among them, Convolutional Neural Network is the most used one. The main objective of this work is to classify images of waste materials using CNN into seven categories (cardboard, glass, metal, organic, paper, plastic, and trash). Then, cardboard, organic, and paper class images are considered biodegradable waste, and other classes are considered non-biodegradable waste. The pre-trained CNN model such as InceptionV3, InceptionResNetV2, Xception, VGG19, MobileNet, ResNet50 and DenseNet201 have been trained and performed fine-tuning on the waste dataset. Among these models, the VGG19 model performed with less accuracy, whereas the InceptionV3 model performed with high learning accuracy. Overall, the obtained result is promising.","PeriodicalId":270700,"journal":{"name":"Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129447689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Triplet Loss based Siamese Networks for Automatic Short Answer Grading 基于三重损失的Siamese网络自动简答评分

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574337

Nagamani Yeruva, Sarada Venna, Hemalatha Indukuri, Mounika Marreddy

引用次数: 1

FIRE 2022 ILSUM Track: Indian Language Summarization FIRE 2022 ILSUM轨道:印度语言摘要

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574328

Shrey Satapara, Bhavan Modha, Sandip J Modha, Parth Mehta

引用次数: 11

Overview of the HASOC Subtrack at FIRE 2022: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages FIRE 2022的HASOC子轨道概述:英语和印度雅利安语言中的仇恨言论和攻击性内容识别

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574326

Thomas Mandl, Sandip J Modha, Gautam Kishore Shahi, Hiren Madhu, Shrey Satapara, Prasenjit Majumder, Johannes Schäfer, Tharindu Ranasinghe, Marcos Zampieri, D. Nandini, A. Jaiswal

{"title":"Overview of the HASOC Subtrack at FIRE 2022: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages","authors":"Thomas Mandl, Sandip J Modha, Gautam Kishore Shahi, Hiren Madhu, Shrey Satapara, Prasenjit Majumder, Johannes Schäfer, Tharindu Ranasinghe, Marcos Zampieri, D. Nandini, A. Jaiswal","doi":"10.1145/3574318.3574326","DOIUrl":"https://doi.org/10.1145/3574318.3574326","url":null,"abstract":"In recent years, the spread of online offensive content has become of great concern, motivating researchers to develop robust systems capable of identifying such content automatically. To carry out a fair evaluation of these systems, several international shared tasks have been organized, providing the community with essential benchmark data and evaluation methods for various languages. Organized since 2019, the HASOC (Hate Speech and Offensive Content Identification) shared task is one of these initiatives. In its fourth iteration, HASOC 2022 included three tasks for English-Hindi codemix, German and Marathi. Tasks 1 and 2 were on conversational hate speech detection. The idea is to detect supporting hate speech, profanity, or other forms of offensiveness depending on the surrounding context of Twitter posts. Task 1 was offered in Hindi-English codemix and German. Task 2 was provided for Hindi-English codemix, and it was focused on further classifying the problematic tweets in conversational hate speech into standalone and contextual hate. This paper presents a brief description of tasks, data, and participation.","PeriodicalId":270700,"journal":{"name":"Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121852142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 83

Findings of shared task on Sentiment Analysis and Homophobia Detection of YouTube Comments in Code-Mixed Dravidian Languages 代码混合德拉威语YouTube评论情感分析与同性恋恐惧症检测的共享任务研究

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574347

Subalalitha Chinnaudayar Navaneethakrishnan, Bharathi Raja Chakravarthi, Kogilavani Shanmugavadivel, Malliga Subramanian, Prasanna Kumar Kumaresan, Bharathi, Lavanya Sambath Kumar, Rahul Ponnusamy

引用次数: 8