Dataset Information

Stopwords in technical language processing.

ABSTRACT: There are increasing applications of natural language processing techniques for information retrieval, indexing, topic modelling and text classification in engineering contexts. A standard component of such tasks is the removal of stopwords, which are uninformative components of the data. While researchers use readily available stopwords lists that are derived from non-technical resources, the technical jargon of engineering fields contains their own highly frequent and uninformative words and there exists no standard stopwords list for technical language processing applications. Here we address this gap by rigorously identifying generic, insignificant, uninformative stopwords in engineering texts beyond the stopwords in general texts, based on the synthesis of alternative statistical measures such as term frequency, inverse document frequency, and entropy, and curating a stopwords dataset ready for technical language processing applications.

SUBMITTER: Sarica S

PROVIDER: S-EPMC8341615 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Stopwords in technical language processing.

Sarica Serhad S Luo Jianxi J

PloS one 20210805 8

There are increasing applications of natural language processing techniques for information retrieval, indexing, topic modelling and text classification in engineering contexts. A standard component of such tasks is the removal of stopwords, which are uninformative components of the data. While researchers use readily available stopwords lists that are derived from non-technical resources, the technical jargon of engineering fields contains their own highly frequent and uninformative words and t ...[more]

PMID: 34351911

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Project description:BACKGROUND:To bridge the gap between symptoms and treatment, constructing case formulations is essential for clinicians. Limited scientific value has been attributed to case formulations because of problems with quality, reliability, and validity. For understanding, communication, and treatment planning beyond each specific clinician-patient dyad, a case formulation must convey valid information concerning the patient, as well as being a reliable source of information regardless of the clinician's theoretical orientation. The first aim of the present study is to explore the completeness of unstructured psychodynamic formulations, according to four components outlined in the Case Formulation Content Coding Method (CFCCM). The second aim is to estimate the reliability of independent formulations and their components, using similarity ratings of matched versus mismatched cases. METHODS:This study explores psychodynamic case formulations as made by two or more experienced clinicians after listening to an evaluation interview. The clinicians structured the formulations freely, with the sole constraint that technical, theory-laden terminology should be avoided. The formulations were decomposed into components after all formulations had been written. RESULTS:The results indicated that most formulations were adequately comprehensive, and that overall reliability of the formulations was high (> 0.70) for both experienced and inexperienced clinician raters, although the lower bound reliability estimate of the formulation component deemed most difficult to rate - inferred mechanisms - was marginal, 0.61. CONCLUSIONS:These results were achieved on case formulations made by experienced clinicians using simple experience-near language and minimizing technical concepts, which indicate a communicative quality in the formulations that make them clinically sound. TRIAL REGISTRATION:linicalTrials.gov Identifier: NCT00423462 . https://doi.org/10.1007/s00432-018-2781-7 ., January 18, 2007.

Dataset Information

Stopwords in technical language processing.

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Stopwords in technical language processing.

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