Abstract Tenders are powerful means of investment of public funds and represent a strategic development resource. Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only. With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration. Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited. Nevertheless, OIE4PA adopts a multilingual approach so it can be applied to several languages by providing appropriate training data. Rather than purely training a classifier on a portion of the extracted relations, the backbone idea of our learning strategy is to put a supervised method based on self-training to the proof and to assess whether or not it improves the performance of the classifier. For evaluation purposes, we built a dataset composed of 2,000 triples which have been manually annotated by two human experts. The in-vitro evaluation shows that OIE4PA achieves a MacroF $$_1$$ 1 equal to 0.89 and a 91 $$%$$ % accuracy. In addition, OIE4PA was used as the pillar of a prototype search engine, which has been evaluated through an in-vivo experiment with positive feedback from 32 final users, obtaining a SUS score equal to 83.98 .
招标是公共资金强有力的投资手段，是一种战略性的发展资源。尽管到目前为止，国家和国际各级政府都在努力将与公共行政部门有关的文件数字化，但大多数信息仍然以非结构化格式提供。为了弥合这一差距，我们提出了OIE4PA，这是我们从公共行政投标中提取和分类关系的最新研究。我们的工作重点是意大利语，其中语言资源的可用性来执行自然语言处理任务是相当有限的。然而，OIE4PA采用多语言方法，因此可以通过提供适当的训练数据将其应用于多种语言。我们的学习策略的核心思想不是单纯地在抽取的部分关系上训练分类器，而是将基于自我训练的监督方法用于证明，并评估它是否提高了分类器的性能。为了评估目的，我们建立了一个由2000个三元组组成的数据集，这些三元组由两名人类专家手工注释。体外评价显示OIE4PA的macroof $$_1$$ 1 = 0.89，达到91 $$%$$ % accuracy. In addition, OIE4PA was used as the pillar of a prototype search engine, which has been evaluated through an in-vivo experiment with positive feedback from 32 final users, obtaining a SUS score equal to 83.98 .
{"title":"OIE4PA: open information extraction for the public administration","authors":"Lucia Siciliani, Eleonora Ghizzota, Pierpaolo Basile, Pasquale Lops","doi":"10.1007/s10844-023-00814-z","DOIUrl":"https://doi.org/10.1007/s10844-023-00814-z","url":null,"abstract":"Abstract Tenders are powerful means of investment of public funds and represent a strategic development resource. Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only. With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration. Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited. Nevertheless, OIE4PA adopts a multilingual approach so it can be applied to several languages by providing appropriate training data. Rather than purely training a classifier on a portion of the extracted relations, the backbone idea of our learning strategy is to put a supervised method based on self-training to the proof and to assess whether or not it improves the performance of the classifier. For evaluation purposes, we built a dataset composed of 2,000 triples which have been manually annotated by two human experts. The in-vitro evaluation shows that OIE4PA achieves a MacroF $$_1$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:msub> <mml:mrow /> <mml:mn>1</mml:mn> </mml:msub> </mml:math> equal to 0.89 and a 91 $$%$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mo>%</mml:mo> </mml:math> accuracy. In addition, OIE4PA was used as the pillar of a prototype search engine, which has been evaluated through an in-vivo experiment with positive feedback from 32 final users, obtaining a SUS score equal to 83.98 .","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312979","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}
Pub Date : 2023-09-18DOI: 10.1007/s10844-023-00813-0
Yuqiang Li, Jianxiang Long, Chun Liu
{"title":"Global-mirror graph network for session-based recommendation","authors":"Yuqiang Li, Jianxiang Long, Chun Liu","doi":"10.1007/s10844-023-00813-0","DOIUrl":"https://doi.org/10.1007/s10844-023-00813-0","url":null,"abstract":"","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135110034","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}
Pub Date : 2023-09-15DOI: 10.1007/s10844-023-00808-x
None Mamta, Asif Ekbal
{"title":"Transformer based multilingual joint learning framework for code-mixed and english sentiment analysis","authors":"None Mamta, Asif Ekbal","doi":"10.1007/s10844-023-00808-x","DOIUrl":"https://doi.org/10.1007/s10844-023-00808-x","url":null,"abstract":"","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135396589","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}
Pub Date : 2023-09-09DOI: 10.1007/s10844-023-00815-y
Qianqian Qi, David J. Hessen, Peter G. M. van der Heijden
Abstract The initial dimensions extracted by latent semantic analysis (LSA) of a document-term matrix have been shown to mainly display marginal effects, which are irrelevant for information retrieval. To improve the performance of LSA, usually the elements of the raw document-term matrix are weighted and the weighting exponent of singular values can be adjusted. An alternative information retrieval technique that ignores the marginal effects is correspondence analysis (CA). In this paper, the information retrieval performance of LSA and CA is empirically compared. Moreover, it is explored whether the two weightings also improve the performance of CA. The results for four empirical datasets show that CA always performs better than LSA. Weighting the elements of the raw data matrix can improve CA; however, it is data dependent and the improvement is small. Adjusting the singular value weighting exponent often improves the performance of CA; however, the extent of the improvement depends on the dataset and the number of dimensions.
{"title":"Improving information retrieval through correspondence analysis instead of latent semantic analysis","authors":"Qianqian Qi, David J. Hessen, Peter G. M. van der Heijden","doi":"10.1007/s10844-023-00815-y","DOIUrl":"https://doi.org/10.1007/s10844-023-00815-y","url":null,"abstract":"Abstract The initial dimensions extracted by latent semantic analysis (LSA) of a document-term matrix have been shown to mainly display marginal effects, which are irrelevant for information retrieval. To improve the performance of LSA, usually the elements of the raw document-term matrix are weighted and the weighting exponent of singular values can be adjusted. An alternative information retrieval technique that ignores the marginal effects is correspondence analysis (CA). In this paper, the information retrieval performance of LSA and CA is empirically compared. Moreover, it is explored whether the two weightings also improve the performance of CA. The results for four empirical datasets show that CA always performs better than LSA. Weighting the elements of the raw data matrix can improve CA; however, it is data dependent and the improvement is small. Adjusting the singular value weighting exponent often improves the performance of CA; however, the extent of the improvement depends on the dataset and the number of dimensions.","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136192587","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}
Pub Date : 2023-09-01DOI: 10.1007/s10844-023-00811-2
Jialin Feng, Hong Li, Zhiyi Yu
{"title":"Enhancing aspect-based sentiment analysis with dependency-attention GCN and mutual assistance mechanism","authors":"Jialin Feng, Hong Li, Zhiyi Yu","doi":"10.1007/s10844-023-00811-2","DOIUrl":"https://doi.org/10.1007/s10844-023-00811-2","url":null,"abstract":"","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85839767","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}
Pub Date : 2023-09-01DOI: 10.1007/s10844-023-00812-1
Lingyun Jin, Jingqiang Chen
{"title":"Self-supervised opinion summarization with multi-modal knowledge graph","authors":"Lingyun Jin, Jingqiang Chen","doi":"10.1007/s10844-023-00812-1","DOIUrl":"https://doi.org/10.1007/s10844-023-00812-1","url":null,"abstract":"","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74458154","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}
Pub Date : 2023-08-28DOI: 10.1007/s10844-023-00810-3
Marcos Fernández-Pichel, Mario Ezra Aragón, Julián Saborido-Patiño, D. Losada
{"title":"Personality trait analysis during the COVID-19 pandemic: a comparative study on social media","authors":"Marcos Fernández-Pichel, Mario Ezra Aragón, Julián Saborido-Patiño, D. Losada","doi":"10.1007/s10844-023-00810-3","DOIUrl":"https://doi.org/10.1007/s10844-023-00810-3","url":null,"abstract":"","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77453867","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}
{"title":"Prompted and integrated textual information enhancing aspect-based sentiment analysis","authors":"Xuefeng Shi, Min Hu, Fuji Ren, Piao Shi, Jiawen Deng, Yiming Tang","doi":"10.1007/s10844-023-00805-0","DOIUrl":"https://doi.org/10.1007/s10844-023-00805-0","url":null,"abstract":"","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87757376","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}
Pub Date : 2023-08-05DOI: 10.1007/s10844-023-00806-z
Fátima Rodrigues, Hugo Correia
{"title":"Semi-supervised and ensemble learning to predict work-related stress","authors":"Fátima Rodrigues, Hugo Correia","doi":"10.1007/s10844-023-00806-z","DOIUrl":"https://doi.org/10.1007/s10844-023-00806-z","url":null,"abstract":"","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2023-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72451019","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}
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