Towards semantically linked multilingual corpus

Zhang, Junsheng; Sun, Yunchuan; Jara, Antonio J.

doi:10.1016/j.ijinfomgt.2015.01.004

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…Efficient classification of data, for example, important for various fields, such as Libraries Science [15], is well supported by XML schemas through different restriction building blocks. In addition, a suitable use of XML schema building blocks can largely support multilingual data, often necessary in the information globalization process [16]. The low-level data collection process is a fundament for higher knowledge creation levels, indicating the importance of data quality, which is often received and stored in XML documents.…”

Section: Knowledge Management and Xml Schemasmentioning

confidence: 99%

Evaluation of XML Schema Support in Knowledge Management

Šumak¹,

Heričko²,

Pušnik³

2020

Frontiers in Artificial Intelligence and Applications

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Well organized data contributes extensively to the classification possibilities and quality of Knowledge Management. XML schemas play an important role in data organization activities, and provide basic foundations for companies and organizations dealing with large amounts of data. In times where knowledge represents the greatest advantage in a competitive economy and is relatively simple to find through different web providers, the quality of internal data structures and efficient management of a company’s valuable information is of the utmost importance. XML schemas are one of the mechanisms that can provide a data organization system in a qualitative manner, and efficient knowledge management as soon as data have been defined or accumulated. A good XML schema support is a way to increase the competitiveness of an organization by ensuring structured data quality and simplifying the Knowledge Management process.

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Section: Knowledge Management and Xml Schemasmentioning

confidence: 99%

Evaluation of XML Schema Support in Knowledge Management

Šumak¹,

Heričko²,

Pušnik³

2020

Frontiers in Artificial Intelligence and Applications

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show abstract

“…In [10], Liu analyzed users' views, attitudes, and feelings in the topics and defined emotional analysis (opinion Complexity 3 mining) as subjective information in 2012. Text analysis and mining are closely related to a multilingual environment, and multilingual corpus construction is an important task for opinion mining in multilingual context [11].…”

Section: Emotional Analysismentioning

confidence: 99%

Tibetan Microblog Emotional Analysis Based on Sequential Model in Online Social Platforms

Qiu

Zhang

et al. 2017

Complexity

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With the development of microblogs, selling and buying appear in online social platforms such as Sina Weibo and Wechat. Besides Mandarin, Tibetan language is also used to describe products and customers' opinions. In this paper, we are interested in analyzing the emotions of Tibetan microblogs, which are helpful to understand opinions and product reviews for Tibetan customers. It is challenging since existing studies paid little attention to Tibetan language. Our key idea is to express Tibetan microblogs as vectors and then classify them. To express microblogs more fully, we select two kinds of features, which are sequential features and semantic features. In addition, our experimental results on the Sina Weibo dataset clearly demonstrate the effectiveness of feature selection and the efficiency of our classification method.

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“…The third way used global features, such as different corpus and languages, to provide context information [10][11][12][13]. Besides, text analysis-based selection of keywords has been used to represent the meanings of scientific documents [14], find the knowledge flows [15], and build multilingual corpus [16]. Coming to the mobile era, information is necessary to be extracted from various resources including texts for event-based information organization [17,18].Patent term extraction research usually used the methods mentioned earlier and applied them to patent text.…”

mentioning

confidence: 99%

Identifying long tail term from large‐scale candidate pairs for big data‐oriented patent analysis

Zhang

Yao

et al. 2016

Concurrency and Computation

Self Cite

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Summary Patent is a very important and valuable type of scientific and technical big data. This paper presents how to mine patent text to obtain valuable information/knowledge from large‐scale candidates obtained from these patents based on massive patent texts. We firstly propose a patent term extraction method using co‐occurrence in the abstract and first‐claim sections of patent records. There are three steps: (1) we extract candidate strings according to our definition of a term; (2) we propose an assumption to verify whether a candidate string is a qualified term or not by using the co‐occurrence of terms in the abstract and first claim; and (3) we use term frequency–inverse document frequencyAUTHOR: TF‐IDF has been defined as “term frequency–inverse document frequency”. Please check if correct. or mutual information to rank and select candidate terms. Secondly, we propose a new method to obtain valuable long tail term from patents. To fulfill the purpose, (1) we firstly build long tail term–common term pair as candidate set; (2) then we evaluate each candidate pair's value; and finally, (3) to demonstrate our method, we give an example on our result. This study provides a new perspective in extracting terms from free texts of patent records and also proposes a new method to obtain valuable long term to aid information analysis with massive patent texts. Copyright © 2016 John Wiley & Sons, Ltd.

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Towards semantically linked multilingual corpus

Cited by 9 publications

References 14 publications

Evaluation of XML Schema Support in Knowledge Management

Evaluation of XML Schema Support in Knowledge Management

Tibetan Microblog Emotional Analysis Based on Sequential Model in Online Social Platforms

Identifying long tail term from large‐scale candidate pairs for big data‐oriented patent analysis

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