Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation最新文献_第2页

Causal Categorization of Mental Health Posts using Transformers 精神卫生站使用变压器的原因分类

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574334

Muskan Garg, Simranjeet Kaur, Ritika Bhardwaj, Aastha Jain, Chandni Saxena

引用次数: 0

Three Metrics for Musical Chord Label Evaluation 音乐和弦标签评价的三个指标

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574335

Andrew Mcleod, Xavier Suermondt, Yannis Rammos, S. Herff, M. Rohrmeier

引用次数: 0

Can we predict useful comments in source codes? - Analysis of findings from Information Retrieval in Software Engineering Track @ FIRE 2022 我们能在源代码中预测有用的注释吗?-软件工程Track @ FIRE 2022中信息检索结果分析

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574329

Srijoni Majumdar, Ayan Bandyopadhyay, P. Das, Paul D. Clough, S. Chattopadhyay, Prasenjit Majumder

引用次数: 4

NrityaManch: An Annotation and Retrieval System for Bharatanatyam Dance Bharatanatyam舞蹈注释与检索系统

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574338

Soumen Paul, Rounak Saha, Swarup Padhi, Srijoni Majumdar, P. Das, K. S. Rao

{"title":"NrityaManch: An Annotation and Retrieval System for Bharatanatyam Dance","authors":"Soumen Paul, Rounak Saha, Swarup Padhi, Srijoni Majumdar, P. Das, K. S. Rao","doi":"10.1145/3574318.3574338","DOIUrl":"https://doi.org/10.1145/3574318.3574338","url":null,"abstract":"This paper presents an annotation and retrieval application named NrityaManch dedicated explicitly to the Indian classical dance. We primarily choose Bharatanatyam dance for the application development. We exploit ontology technique which captures dance image’s annotation details and structurally organizes the dance database. An OWL2 ontology is developed in Protégé 5.5.0 which is validated using HermiT 1.4.3.456 reasoner to maintain consistency. A user interface is provided for the manual annotation of dance images. Initially, we focus on dancer details, dance details, and elements of static dance posture like hasta mudra during the annotation. All annotation details are saved in RDF/XML file. A search window is provided, which facilitates two types of search - natural language query search and tight query search. Named Entity Recognition (NER) pipeline mechanism is utilized in this work which facilitates keyword extraction from natural language queries. A SPARQL query is automatically generated by the system which is applied to the RDF corpus in order to retrieve distinct images. The NER pipeline mechanism achieves an accuracy of 80% for our dance dataset. The system achieves an average f-score value of 0.8547 for the retrieval functionality. The proposed system intends to help dance learners to find dance resources in a dedicated place and will also help in Indian classical dance preservation.","PeriodicalId":270700,"journal":{"name":"Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation","volume":"107 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":"115449047","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

“If you can’t beat them, join them”: A Word Transformation based Generalized Skip-gram for Embedding Compound Words “打不过他们，就加入他们”:一种基于词变换的复合词嵌入广义跳跃图

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574346

Debasis Ganguly, Shripad Bhat, Chandan Biswas

{"title":"“If you can’t beat them, join them”: A Word Transformation based Generalized Skip-gram for Embedding Compound Words","authors":"Debasis Ganguly, Shripad Bhat, Chandan Biswas","doi":"10.1145/3574318.3574346","DOIUrl":"https://doi.org/10.1145/3574318.3574346","url":null,"abstract":"While a class of data-driven approaches has been shown to be effective in embedding words of languages that are relatively simple as per inflections and compounding characteristics (e.g. English), an open area of investigation is ways of integrating language-specific characteristics within the framework of an embedding model. Standard word embedding approaches, such as word2vec, Glove etc. embed each word into a high dimensional dense vector. However, these approaches may not adequately capture the inherent linguistic phenomenon namely that of word compounding. We propose a stochastic word transformation based generalization of the skip-gram algorithm, which seeks to potentially improve the representation of the compositional compound words by leveraging information from the contexts of their constituents. Our experiments show that addressing the compounding effect of a language as a part of the word embedding objective outperforms existing methods of compounding-specific post-transformation based approaches on word semantics prediction and word polarity prediction tasks.","PeriodicalId":270700,"journal":{"name":"Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation","volume":"50 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":"117169239","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

Extracting Ironic Tweets using Experts Model 利用专家模型提取讽刺推文

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-12-09 DOI: 10.1145/3574318.3574331

Nagamani Yeruva, Sarada Venna, Hemalatha Indukuri, Mounika Marreddy

{"title":"Extracting Ironic Tweets using Experts Model","authors":"Nagamani Yeruva, Sarada Venna, Hemalatha Indukuri, Mounika Marreddy","doi":"10.1145/3574318.3574331","DOIUrl":"https://doi.org/10.1145/3574318.3574331","url":null,"abstract":"Posts on Twitter allow users to express ideas and opinions very dynamically. This high volume of data provides relevant clues about the public judgment on a specific product, event, service, etc. While traditional sentiment analysis primarily focuses on classifying the sentiment in general (positive or negative) or at an aspect level (very positive, low negative, and so on) and cannot exploit the intensity information. Recently, the problem of irony detection in social media has been proven to be pervasive among research enthusiasts, posing a challenge to sentiment analysis systems. Moreover, the figurative use of language has received scarce attention from the computational linguistic research point of view. This paper proposes an architecture, the Experts Model, inspired by the standard Mixture of Experts (MoE) model. The key idea here is that each expert learns different sets of features from the feature vector, which helps in better irony detection (Ironic vs. Non-ironic - SemEval-2018 subtask A) and ironic type detection (Verbal irony with vs. without polarity contrast vs. Situational irony vs. Non-irony - SemEval-2018 subtask B) from the tweet. We compared our Experts Model’s results with baseline results along with the top five performers of SemEval-2018 Task-3, Ironic detection. The experimental results show that our proposed approach deals with the ironic detection problem and stands at the top-3 results. We opted for a transfer learning approach by applying our proposed model on three different datasets #ironic, #sarcasm, and #humor, and we achieved a better F1-score.","PeriodicalId":270700,"journal":{"name":"Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation","volume":"57 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":"132065060","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

Evaluating Impact of Social Media Posts by Executives on Stock Prices 评估高管在社交媒体上发帖对股价的影响

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 2022-11-01 DOI: 10.1145/3574318.3574339

Anubhav Sarkar, Swagata Chakraborty, Sohom Ghosh, Sudipta Naskar

引用次数: 0

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation 第十四届信息检索评估论坛年会论文集

Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation Pub Date : 1900-01-01 DOI: 10.1145/3574318

引用次数: 1