Papers with term extraction

4 papers
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains (P19-1)

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Challenge: Existing models for diachronic and synchronic detection of lexical semantic divergences are superficial and lack of comparison.
Approach: They propose to extend benchmark models on a common state-of-the-art evaluation task . they also demonstrate that the same evaluation task and modelling approaches can be utilised for synchronic detection of domain-specific sense divergences in the field of term extraction.
Outcome: The proposed model can be utilised for the detection of domain-specific sense divergences in the field of term extraction.
A Term Extraction Approach to Survey Analysis in Health Care (2020.lrec-1)

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Challenge: a new study examines the impact of customer feedback on health care organizations . the results of the 2017 Irish National Inpatient Survey are compared to a manual framework .
Approach: They propose an approach to patient experience using free text questions from the 2017 Irish National Inpatient Survey campaign.
Outcome: The proposed approach to patient experience is based on the results of the 2017 Irish National Inpatient Survey.
A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents (L18-1)

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Challenge: Terms are notoriously difficult to identify, both automatically and manually.
Approach: They propose a method to annotate terms manually from a comparable corpus . they show that the gold standard provides a tool for evaluation and a rich source of information .
Outcome: The proposed method provides a tool for evaluation and rich source of information about terms.
Developing an Arabic Infectious Disease Ontology to Include Non-Standard Terminology (2020.lrec-1)

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Challenge: Existing ontologies for Arabic are difficult due to the lack of resources and the nature of the Arabic language.
Approach: They propose to build an Arabic ontology that integrates scientific vocabularies with informal equivalents.
Outcome: The proposed ontology integrates scientific vocabularies with informal equivalents in Arabic . it will be automatically generated but the results will be evaluated by a domain expert .

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