Although much is known about the linguistic function of vowel nasality, whether contrastive (as in French) or coarticulatory (as in English), and much effort has gone into identifying potential correlates for the phenomenon, this study examines these proposed features to find the optimal acoustic feature(s) for nasality measurement. To this end, a corpus of 4778 oral and nasal vowels in English and French was collected, and data for 22 features were extracted. A series of linear mixed-effects regressions highlighted three promising features with large oral-to-nasal feature differences and strong effects relative to normal oral vowel variability: A1-P0, F1's bandwidth, and spectral tilt. However, these three features, particularly A1-P0, showed considerable variation in baseline and range across speakers and vowels within each language. Moreover, although the features were consistent in direction across both languages, French speakers' productions showed markedly stronger effects, and showed evidence of spectral tilt beyond the nasal norm being used to enhance the oral-nasal contrast. These findings strongly suggest that the acoustic nature of vowel nasality is both language- and speaker-specific, and that, like vowel formants, nasality measurements require speaker normalization for across-speaker comparison, and that these acoustic properties should not be taken as constant across different languages.

Studies examining articulatory characteristics of pauses have identified language-specific postures of the vocal tract in inter-utterance pauses and different articulatory patterns in grammatical and non-grammatical pauses. Pause postures-specific articulatory movements that occur during pauses at strong prosodic boundaries-have been identified for Greek and German. However, the cognitive function of these articulations has not been examined so far. We start addressing this question by investigating the effect of 1) utterance type and 2) planning on pause posture occurrence and properties in American English. We first examine whether pause postures exist in American English. In an electromagnetic articulometry study, seven participants produced sentences varying in linguistic structure (stress, boundary, sentence type). To determine the presence of pause postures, as well as to lay the groundwork for their future automatic annotation and detection, a Support Vector Machine Classifier was built to identify pause postures. Results show that pause postures exist for all speakers in this study but that the frequency of occurrence is speaker dependent. Across participants, we find that there is a stable relationship between the pause posture and other events (boundary tones and vowels) at prosodic boundaries, parallel to previous work in Greek. We find that the occurrence of pause postures is not systematically related to utterance type. Lastly, pause postures increase in frequency and duration as utterance length increases, suggesting that pause postures are at least partially related to speech planning processes.

This study documents the relation between f0 and prevoicing in the production and perception of plosive voicing in Afrikaans. Acoustic data show that Afrikaans speakers differed in how likely they were to produce prevoicing to mark phonologically voiced plosives, but that all speakers produced large and systematic f0 differences after phonologically voiced and voiceless plosives to convey the contrast between the voicing categories. This pattern is mirrored in these same participants’ perception: although some listeners relied more than others on prevoicing as a perceptual cue, all listeners used f0 (especially in the absence of prevoicing) to perceptually differentiate historically voiced and voiceless plosives. This variation in the speech community is shown to be generationally structured such that older speakers were more likely than younger speakers to produce prevoicing, and to rely on prevoicing perceptually. These patterns are consistent with generationally determined differential cue weighting in the speech community and with an ongoing sound change in which the original consonantal voicing contrast is being replaced by a tonal contrast on the following vowel.

This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, "the THYME Guidelines to ISO-TimeML (THYME-TimeML)". To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.

Objective

To 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.

Methods

Manual 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.

Results

The 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.

Conclusions

This 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.