ObjectiveTo create annotated clinical narratives with layers of syntactic and semantic labels to facilitate advances in clinical natural language processing (NLP). To develop NLP algorithms and open source components.MethodsManual annotation of a clinical narrative corpus of 127 606 tokens following the Treebank schema for syntactic information, PropBank schema for predicate-argument structures, and the Unified Medical Language System (UMLS) schema for semantic information. NLP components were developed.ResultsThe final corpus consists of 13 091 sentences containing 1772 distinct predicate lemmas. Of the 766 newly created PropBank frames, 74 are verbs. There are 28 539 named entity (NE) annotations spread over 15 UMLS semantic groups, one UMLS semantic type, and the Person semantic category. The most frequent annotations belong to the UMLS semantic groups of Procedures (15.71%), Disorders (14.74%), Concepts and Ideas (15.10%), Anatomy (12.80%), Chemicals and Drugs (7.49%), and the UMLS semantic type of Sign or Symptom (12.46%). Inter-annotator agreement results: Treebank (0.926), PropBank (0.891–0.931), NE (0.697–0.750). The part-of-speech tagger, constituency parser, dependency parser, and semantic role labeler are built from the corpus and released open source. A significant limitation uncovered by this project is the need for the NLP community to develop a widely agreed-upon schema for the annotation of clinical concepts and their relations.ConclusionsThis project takes a foundational step towards bringing the field of clinical NLP up to par with NLP in the general domain. The corpus creation and NLP components provide a resource for research and application development that would have been previously impossible.

Recent research on the relationship between grammatical aspect and motion event construal shows that speakers of non-aspect languages (e.g. German, Swedish) attend to event endpoints more than speakers of aspect languages (e.g. English, Spanish) in non-verbal categorization tasks Flecken et al., 2013;von Stutterheim et al., 2012). In this paper we take a perceptual learning approach to the Whorfian hypothesis, training native speakers of English to categorize events either in an English-like way (same-language bias) or in a Swedish-like way (other-language bias), with and without verbal interference in English. Results showed that successful learning occurred in both language conditions, and that verbal interference disrupted learning only in the condition where the perceptual dimension to be learned was also salient in the participant's native language, but not in the condition where it was not. This suggests that individuals may recruit verbal processes online for the purposes of classification more readily when the stimuli to be classified are also habitually encoded in the native language, but may rely on language-independent perceptual mechanisms when learning to classify stimuli not habitually encoded in their native language.

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The witches in Shakespeare’s Macbeth equivocate between the demons of random malevolence and ordinary (if exceptionally nasty) old women; and both King James I, whose book on witchcraft may have influenced Shakespeare, and A. W. Schlegel, whose essay on Macbeth certainly influenced Verdi, also stress this ambiguity. In his treatment of Lady Macbeth, Verdi uses certain musical patterns associated with the witches; and like the witches, who sound sometimes tame and frivolous, sometimes like incarnations of supernatural evil, Lady Macbeth hovers insecurely between roles: she is a hybrid of ambitious wife and agent of hell.