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Detecting event-related recurrences by symbolic analysis: applications to human language processing.

ABSTRACT: Quasi-stationarity is ubiquitous in complex dynamical systems. In brain dynamics, there is ample evidence that event-related potentials (ERPs) reflect such quasi-stationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study, we elaborate a recent approach for detecting quasi-stationary states as recurrence domains by means of recurrence analysis and subsequent symbolization methods. We address two pertinent problems of contemporary recurrence analysis: optimizing the size of recurrence neighbourhoods and identifying symbols from different realizations for sequence alignment. As possible solutions for these problems, we suggest a maximum entropy criterion and a Hausdorff clustering algorithm. The resulting recurrence domains for single-subject ERPs are obtained as partition cells reflecting quasi-stationary brain states.

SUBMITTER: Beim Graben P

PROVIDER: S-EPMC4281863 | biostudies-other | 2015 Feb

REPOSITORIES: biostudies-other

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Detecting event-related recurrences by symbolic analysis: applications to human language processing.

Beim Graben Peter P Hutt Axel A

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 20150201 2034

Quasi-stationarity is ubiquitous in complex dynamical systems. In brain dynamics, there is ample evidence that event-related potentials (ERPs) reflect such quasi-stationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study, we elaborate a recent approach for detecting quasi-stationary states as recurrence domains by means of recurrence analysis and subsequent symbolization methods. We address two pertinent problems of contemporary ...[more]

PMID: 25548270

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Project description:Second language (L2) learners experience challenges when word meanings differ across L1 and L2, and often display crosslinguistic influence (CLI) in speech production. In contrast, studies of online comprehension show more mixed results. Therefore, this study explored how L2 learners process fine-grained L2 verb semantics in the domain of caused motion (placement) and specifically the impact of having similar vs. non-similar semantics in the L1 and L2. Specifically, we examined English (20) and German (21) L2 learners of Swedish and native Swedish speakers (16) and their online neurophysiological processing and offline appropriateness ratings of three Swedish placement verbs obligatory for placement supported from below: sätta "set," ställa "stand," and lägga "lay." The learners' L1s differed from Swedish in that their placement verbs either shared or did not share semantic characteristics with the target language. English has a general placement verb put, whereas German has specific verbs similar but not identical to Swedish, stellen "set/stand" and legen "lay." Event-related potentials (ERPs) were recorded while participants watched still frames (images) of objects being placed on a table and listened to sentences describing the event with verbs that either matched the image or not. Participants also performed an offline appropriateness rating task. Both tasks suggested CLI. English learners' appropriateness ratings of atypical verb use differed from those of both native Swedish speakers' and German learners, with no difference in the latter pair. Similarly, German learners' ERP effects were more similar to those of the native Swedish speakers (increased lateral negativity to atypical verb use) than to those of the English learners (increased positivity to atypical verb use). The results of this explorative study thus suggest CLI both offline and online with similarity between L1 and L2 indicating more similar processing and judgments, in line with previous production findings, but in contrast to previous ERP work on semantic L2 processing.

Project description:According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of public health agents sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural language processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at the RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles' key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We extracted the key country and disease using a heuristic with good results. We trained a naive Bayes classifier to find the key date and confirmed-case count, using the RKI's EBS database as labels which performed modestly. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using bag-of-words, document and word embeddings. The best classifier, a logistic regression, achieved a sensitivity of 0.82 and an index balanced accuracy of 0.61. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code and data are publicly available under open licenses.

Dataset Information

Detecting event-related recurrences by symbolic analysis: applications to human language processing.

Publications

Detecting event-related recurrences by symbolic analysis: applications to human language processing.

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