Morpho-syntactic Tagging of the Spanish C-ORAL-ROM Corpus

Sandoval, Antonio Moreno; Guirao, José María

doi:10.1075/ubli.5.15mor

Cited by 10 publications

(4 citation statements)

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“…For this reason, state-of-the-art lexicons [ 14 , 36 , 49 ] gather verb terms, and we proceed similarly in MedLexSp. From a list of medical verbs, we generated conjugated variants by using a python script and the lexicon of a Spanish part-of-speech tagger [ 50 ]: e.g. sangrar (‘to bleed’) sangra (‘he/she/it bleeds’), sangrando (‘bleeding’), sangrado (‘bled’)... Then, the CUI of each noun term was assigned to the corresponding verb term.…”

Section: Construction and Contentmentioning

confidence: 99%

“… The subset of medical terms encoded in the lexicon of the SPACCC PoS tagger [ 72 ] was processed and their linguistic information was added to MedLexSp. The GRAMPAL tagger [ 50 ] was applied to predict the part-of-speech of mono-word terms for which no morphological data were obtained using the previous methods. Lastly, with regard to multi-words, the procedure was to leverage the information of the head word, given that the head determines the analysis of the constituent.…”

Section: Construction and Contentmentioning

confidence: 99%

“…The GRAMPAL tagger [ 50 ] was applied to predict the part-of-speech of mono-word terms for which no morphological data were obtained using the previous methods.…”

Section: Construction and Contentmentioning

confidence: 99%

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MedLexSp – a medical lexicon for Spanish medical natural language processing

Llanos

2023

J Biomed Semant

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Background Medical lexicons enable the natural language processing (NLP) of health texts. Lexicons gather terms and concepts from thesauri and ontologies, and linguistic data for part-of-speech (PoS) tagging, lemmatization or natural language generation. To date, there is no such type of resource for Spanish. Construction and content This article describes an unified medical lexicon for Medical Natural Language Processing in Spanish. MedLexSp includes terms and inflected word forms with PoS information and Unified Medical Language System$$^{\circledR }$$ ® (UMLS) semantic types, groups and Concept Unique Identifiers (CUIs). To create it, we used NLP techniques and domain corpora (e.g. MedlinePlus). We also collected terms from the Dictionary of Medical Terms from the Spanish Royal Academy of Medicine, the Medical Subject Headings (MeSH), the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT), the Medical Dictionary for Regulatory Activities Terminology (MedDRA), the International Classification of Diseases vs. 10, the Anatomical Therapeutic Chemical Classification, the National Cancer Institute (NCI) Dictionary, the Online Mendelian Inheritance in Man (OMIM) and OrphaData. Terms related to COVID-19 were assembled by applying a similarity-based approach with word embeddings trained on a large corpus. MedLexSp includes 100 887 lemmas, 302 543 inflected forms (conjugated verbs, and number/gender variants), and 42 958 UMLS CUIs. We report two use cases of MedLexSp. First, applying the lexicon to pre-annotate a corpus of 1200 texts related to clinical trials. Second, PoS tagging and lemmatizing texts about clinical cases. MedLexSp improved the scores for PoS tagging and lemmatization compared to the default Spacy and Stanza python libraries. Conclusions The lexicon is distributed in a delimiter-separated value file; an XML file with the Lexical Markup Framework; a lemmatizer module for the Spacy and Stanza libraries; and complementary Lexical Record (LR) files. The embeddings and code to extract COVID-19 terms, and the Spacy and Stanza lemmatizers enriched with medical terms are provided in a public repository.

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Section: Construction and Contentmentioning

confidence: 99%

Section: Construction and Contentmentioning

confidence: 99%

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MedLexSp – a medical lexicon for Spanish medical natural language processing

Llanos

2023

J Biomed Semant

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“…The Spanish dataset is taken from the FinT-esp corpus (Moreno-Sandoval et al, 2020) and consists of 262 documents with a distribution utterly similar to the Greek dataset (see Table 3). The originals were carefully edited by hand, and fragments not containing the narrative (tables, footnotes, headers, etc.)…”

Section: Spanish Datasetmentioning

confidence: 99%