NERosetta for the Named Entity Multi-lingual Space

Krstev, Cvetana; Zečević, Anđelka; Vitas, Duško; Kyriacopoulou, Tita

doi:10.1007/978-3-319-43808-5_25

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Markers can be used within regular expressions, for some simpler queries over the corpus or for producing queries by constructing automata with more complex requirements. An example of the research in which markers were successfully used for IE is the system for named entity recognition NERosetta (names of institutions, personal names, streets and so forth) (Krstev et al, 2013). The use of markers is possible for text processing purposes to extract domain terminology, measuring units, creating relations between words and so on.…”

Section: Managing Mining Project Documentationmentioning

confidence: 99%

Managing mining project documentation using human language technology

Tomašević

Stanković

Utvić

et al. 2018

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Purpose This paper aims to develop a system, which would enable efficient management and exploitation of documentation in electronic form, related to mining projects, with information retrieval and information extraction (IE) features, using various language resources and natural language processing. Design/methodology/approach The system is designed to integrate textual, lexical, semantic and terminological resources, enabling advanced document search and extraction of information. These resources are integrated with a set of Web services and applications, for different user profiles and use-cases. Findings The use of the system is illustrated by examples demonstrating keyword search supported by Web query expansion services, search based on regular expressions, corpus search based on local grammars, followed by extraction of information based on this search and finally, search with lexical masks using domain and semantic markers. Originality/value The presented system is the first software solution for implementation of human language technology in management of documentation from the mining engineering domain, but it is also applicable to other engineering and non-engineering domains. The system is independent of the type of alphabet (Cyrillic and Latin), which makes it applicable to other languages of the Balkan region related to Serbian, and its support for morphological dictionaries can be applied in most morphologically complex languages, such as Slavic languages. Significant search improvements and the efficiency of IE are based on semantic networks and terminology dictionaries, with the support of local grammars.

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Section: Managing Mining Project Documentationmentioning

confidence: 99%

Managing mining project documentation using human language technology

Tomašević

Stanković

Utvić

et al. 2018

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“…Another set of algorithms addresses the problem of fully automated (without human validation) large-scale entity extraction. These algorithms target multiple types of entities and have been deployed in several domains (Li et al , 2020), including bioinformatics (Spasić et al , 2013), social media (Lu et al , 2018), local grammar (Krstev et al , 2014) and languages (Krstev et al , 2013; Rehm and Uszkoreit, 2012). The large number of entities recognized from each document makes it impractical to involve authors in the validation of the results.…”

Section: Introductionmentioning

confidence: 99%

Framework for entity extraction with verification: application to inference of data set usage in research publications

Nestorov

Bačić

Jukić

et al. 2022

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Purpose The purpose of this paper is to propose an extensible framework for extracting data set usage from research articles. Design/methodology/approach The framework uses a training set of manually labeled examples to identify word features surrounding data set usage references. Using the word features and general entity identifiers, candidate data sets are extracted and scored separately at the sentence and document levels. Finally, the extracted data set references can be verified by the authors using a web-based verification module. Findings This paper successfully addresses a significant gap in entity extraction literature by focusing on data set extraction. In the process, this paper: identified an entity-extraction scenario with specific characteristics that enable a multiphase approach, including a feasible author-verification step; defined the search space for word feature identification; defined scoring functions for sentences and documents; and designed a simple web-based author verification step. The framework is successfully tested on 178 articles authored by researchers from a large research organization. Originality/value Whereas previous approaches focused on completely automated large-scale entity recognition from text snippets, the proposed framework is designed for a longer, high-quality text, such as a research publication. The framework includes a verification module that enables the request validation of the discovered entities by the authors of the research publications. This module shares some similarities with general crowdsourcing approaches, but the target scenario increases the likelihood of meaningful author participation.

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NERosetta for the Named Entity Multi-lingual Space

Cited by 2 publications

References 7 publications

Managing mining project documentation using human language technology

Managing mining project documentation using human language technology

Framework for entity extraction with verification: application to inference of data set usage in research publications

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