Nominalization and Alternations in Biomedical Language

Abstract

BackgroundThis paper presents data on alternations in the argument structure of common domain-specific verbs and their associated verbal nominalizations in the PennBioIE corpus. Alternation is the term in theoretical linguistics for variations in the surface syntactic form of verbs, e.g. the different forms of stimulate in FSH stimulates follicular development and follicular development is stimulated by FSH. The data is used to assess the implications of alternations for biomedical text mining systems and to test the fit of the sublanguage model to biomedical texts.Methodology/Principal FindingsWe examined 1,872 tokens of the ten most common domain-specific verbs or their zero-related nouns in the PennBioIE corpus and labelled them for the presence or absence of three alternations. We then annotated the arguments of 746 tokens of the nominalizations related to these verbs and counted alternations related to the presence or absence of arguments and to the syntactic position of non-absent arguments. We found that alternations are quite common both for verbs and for nominalizations. We also found a previously undescribed alternation involving an adjectival present participle.Conclusions/SignificanceWe found that even in this semantically restricted domain, alternations are quite common, and alternations involving nominalizations are exceptionally diverse. Nonetheless, the sublanguage model applies to biomedical language. We also report on a previously undescribed alternation involving an adjectival present participle.

What can natural language processing do for clinical decision support?

“…Several resources contain relation types for the biomedical domain, for example, the UMLS Semantic Network14 that defines binary relations allowed between the UMLS semantic types. Although annotation efforts sometimes include relations [46, 60], it remains to be seen whether explicitly stated relations occur in clinical narrative regularly and frequently enough to be necessary or useful for clinical decision support and whether experience in relation extraction from the literature [61, 62] can be leveraged in clinical text processing.…”

Workshops

“…We perform trigger identification using the assumption that events are triggered in text either by verbal or nominal predicates [11].…”

Exploiting semantic annotations for open information extraction: an experience in the biomedical domain

“…We are aware of the variety of syntactical patterns used to express relations in the biomedical domain. These range from simple < subject, predicate, object > patterns where the predicate is a verbal form (e.g., < inf luenza, induces, asthma >) to verb nominalizations and complex alternations both of verbs and nouns [13]. Table 2 shows some examples of the latter.…”