Dataset Information

Modeling the predictive potential of extralinguistic context with script knowledge: The case of fragments.

ABSTRACT: We describe a novel approach to estimating the predictability of utterances given extralinguistic context in psycholinguistic research. Predictability effects on language production and comprehension are widely attested, but so far predictability has mostly been manipulated through local linguistic context, which is captured with n-gram language models. However, this method does not allow to investigate predictability effects driven by extralinguistic context. Modeling effects of extralinguistic context is particularly relevant to discourse-initial expressions, which can be predictable even if they lack linguistic context at all. We propose to use script knowledge as an approximation to extralinguistic context. Since the application of script knowledge involves the generation of prediction about upcoming events, we expect that scrips can be used to manipulate the likelihood of linguistic expressions referring to these events. Previous research has shown that script-based discourse expectations modulate the likelihood of linguistic expressions, but script knowledge has often been operationalized with stimuli which were based on researchers' intuitions and/or expensive production and norming studies. We propose to quantify the likelihood of an utterance based on the probability of the event to which it refers. This probability is calculated with event language models trained on a script knowledge corpus and modulated with probabilistic event chains extracted from the corpus. We use the DeScript corpus of script knowledge to obtain empirically founded estimates of the likelihood of an event to occur in context without having to resort to expensive pre-tests of the stimuli. We exemplify our method at a case study on the usage of nonsentential expressions (fragments), which shows that utterances that are predictable given script-based extralinguistic context are more likely to be reduced.

SUBMITTER: Lemke R

PROVIDER: S-EPMC7877649 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modeling the predictive potential of extralinguistic context with script knowledge: The case of fragments.

Lemke Robin R Schäfer Lisa L Reich Ingo I

PloS one 20210211 2

We describe a novel approach to estimating the predictability of utterances given extralinguistic context in psycholinguistic research. Predictability effects on language production and comprehension are widely attested, but so far predictability has mostly been manipulated through local linguistic context, which is captured with n-gram language models. However, this method does not allow to investigate predictability effects driven by extralinguistic context. Modeling effects of extralinguistic ...[more]

PMID: 33571248

Similar Datasets

Project description:BackgroundGiven the significant impact on public health and drug development, drug safety has been a focal point and research emphasis across multiple disciplines in addition to scientific investigation, including consumer advocates, drug developers and regulators. Such a concern and effort has led numerous databases with drug safety information available in the public domain and the majority of them contain substantial textual data. Text mining offers an opportunity to leverage the hidden knowledge within these textual data for the enhanced understanding of drug safety and thus improving public health.MethodsIn this proof-of-concept study, topic modeling, an unsupervised text mining approach, was performed on the LiverTox database developed by National Institutes of Health (NIH). The LiverTox structured one document per drug that contains multiple sections summarizing clinical information on drug-induced liver injury (DILI). We hypothesized that these documents might contain specific textual patterns that could be used to address key DILI issues. We placed the study on drug-induced acute liver failure (ALF) which was a severe form of DILI with limited treatment options.ResultsAfter topic modeling of the "Hepatotoxicity" sections of the LiverTox across 478 drug documents, we identified a hidden topic relevant to Hy's law that was a widely-accepted rule incriminating drugs with high risk of causing ALF in humans. Using this topic, a total of 127 drugs were further implicated, 77 of which had clear ALF relevant terms in the "Outcome and management" sections of the LiverTox. For the rest of 50 drugs, evidence supporting risk of ALF was found for 42 drugs from other public databases.ConclusionIn this case study, the knowledge buried in the textual data was extracted for identification of drugs with potential of causing ALF by applying topic modeling to the LiverTox database. The knowledge further guided identification of drugs with the similar potential and most of them could be verified and confirmed. This study highlights the utility of topic modeling to leverage information within textual drug safety databases, which provides new opportunities in the big data era to assess drug safety.

Project description:Background: To investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis using multifactorial predictive models. Materials and Methods: Baseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans. Spearman's correlation (Rs) test was used to correlate clinical and dosimetric variables with toxicities. Toxicity prediction models were built using least absolute shrinkage and selection operator (LASSO) logistic regression on 1,000 bootstrapped datasets. Results: Grade ≥2 esophagitis and pneumonitis developed in 61 (23.6%) and 36 (14.0%) patients, respectively. The median A2M level was 191 mg/dL (range: 94-511). Never/former/current smoker status was 47 (18.2%)/179 (69.4%)/32 (12.4%). We found a significant negative univariate correlation between baseline A2M levels and esophagitis (Rs = -0.18/p = 0.003) and between A2M and smoking status (Rs = 0.13/p = 0.04). Further significant parameters for grade ≥2 esophagitis included age (Rs = -0.32/p < 0.0001), chemotherapy use (Rs = 0.56/p < 0.0001), dose per fraction (Rs = -0.57/p < 0.0001), total dose (Rs = 0.35/p < 0.0001), and several other dosimetric variables with Rs > 0.5 (p < 0.0001). The only significant non-dosimetric parameter for grade ≥2 pneumonitis was sex (Rs = -0.32/p = 0.037) with higher risk for women. For pneumonitis D15 (lung) (Rs = 0.19/p = 0.006) and D45 (heart) (Rs = 0.16/p = 0.016) had the highest correlation. LASSO models applied on the validation data were statistically significant and resulted in areas under the receiver operating characteristic curve of 0.84 (esophagitis) and 0.78 (pneumonitis). Multivariate predictive models did not require A2M to reach maximum predictive power. Conclusion: This is the first study showing a likely association of higher baseline A2M values with lower risk of radiation esophagitis and with smoking status. However, the baseline A2M level was not a significant risk factor for radiation pneumonitis.

Dataset Information

Modeling the predictive potential of extralinguistic context with script knowledge: The case of fragments.

Publications

Modeling the predictive potential of extralinguistic context with script knowledge: The case of fragments.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets