Automatic Taxonomy Generation: A Use-Case in the Legal Domain

Robin, Cécile; O’Neill, James A.; Buitelaar, Paul

doi:10.1007/978-3-030-66527-2_23

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Typically, the starting point for the taxonomy construction is a text corpus that accurately characterizes a specific domain. Robin et al [ 15 ] describes a methodology to automatically generate a taxonomy of legal concepts, and apply this methodology on a corpus consisting of statutory instruments for the UK, Wales, Scotland, and Northern Ireland laws. Bai et al [ 16 ] first propose a method to construct business taxonomies automatically from corporate reports.…”

Section: Related Workmentioning

confidence: 99%

“…Word embedding, NLP and clustering techniques have been applied to the textual descriptions of the surveyed initiatives to categorize them into semantically homogeneous groups. To the best of our knowledge, such techniques have been used in different domains, such as law [ 15 ] and economics [ 16 ], but this is one of the first attempts to clustering initiatives against Covid19 from textual descriptions.…”

Section: Introductionmentioning

confidence: 99%

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Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy

et al. 2022

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The Covid19 pandemic has significantly impacted on our lives, triggering a strong reaction resulting in vaccines, more effective diagnoses and therapies, policies to contain the pandemic outbreak, to name but a few. A significant contribution to their success comes from the computer science and information technology communities, both in support to other disciplines and as the primary driver of solutions for, e.g., diagnostics, social distancing, and contact tracing. In this work, we surveyed the Italian computer science and engineering community initiatives against the Covid19 pandemic. The 128 responses thus collected document the response of such a community during the first pandemic wave in Italy (February-May 2020), through several initiatives carried out by both single researchers and research groups able to promptly react to Covid19, even remotely. The data obtained by the survey are here reported, discussed and further investigated by Natural Language Processing techniques, to generate semantic clusters based on embedding representations of the surveyed activity descriptions. The resulting clusters have been then used to extend an existing Covid19 taxonomy with the classification of related research activities in computer science and information technology areas, summarizing this work contribution through a reproducible survey-to-taxonomy methodology.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy

et al. 2022

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“…A general problem with domain-specific corpora, however, is that they may not meet requirements for token size. In a study of Robin, O'Neill, and Buitelaar (2017), this lead to the rejection of embeddings trained on domain-specific corpora in favor of embeddings trained on general-purpose corpora, since these performed better in a down-stream task. Furthermore, Roy, Park, and Pan (2017) claim that domain-specific corpora may not suffice to guarantee reliable domain-specific embeddings, since even their items can be sparse and not (co-)occur often enough although they are of relevance to the domain.…”

Section: Related Workmentioning

confidence: 99%

Harbsafe-162. A Domain-Specific Data Set for the Intrinsic Evaluation of Semantic Representations for Terminological Data

Arndt¹,

Schnäpp²

2020

Preprint

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The article presents Harbsafe-162, a domain-specific data set for evaluating distributional semantic models. It originates from a cooperation by Technische Universität Braunschweig and the German Commission for Electrical, Electronic & Information Technologies of DIN and VDE, the Harbsafe project. One objective of the project is to apply distributional semantic models to terminological entries, that is, complex lexical data comprising of at least one or several terms, term phrases and a definition. This application is needed to solve a more complex problem: the harmonization of terminologies of standards and standards bodies (i.e. resolution of doublettes and inconsistencies). Due to a lack of evaluation data sets for terminological entries, the creation of Harbsafe-162 was a necessary step towards harmonization assistance. Harbsafe-162 covers data from nine electrotechnical standards in the domain of functional safety, IT security, and dependability. An intrinsic evaluation method in the form of a similarity rating task has been applied in which two linguists and three domain experts from standardization participated. The data set is used to evaluate a specific implementation of an established sentence embedding model. This implementation proves to be satisfactory for the domain-specific data so that further implementations for harmonization assistance may be brought forward by the project. Considering recent criticism on intrinsic evaluation methods, the article concludes with an evaluation of Harbsafe-162 and joins a more general discussion about the nature of similarity rating tasks. Harbsafe-162 has been made available for the community.

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Automatic Taxonomy Generation: A Use-Case in the Legal Domain

Cited by 2 publications

References 7 publications

Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy

Covid19/IT the digital side of Covid19: A picture from Italy with clustering and taxonomy

Harbsafe-162. A Domain-Specific Data Set for the Intrinsic Evaluation of Semantic Representations for Terminological Data

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