Role of expectation and working memory constraints in Hindi comprehension: An eyetracking corpus analysis

We used the Potsdam-Allahabad Hindi eye-tracking corpus to investigate the role of word-level and sentence-level factors during sentence comprehension in Hindi. Extending previous work that used this eye-tracking data, we investigate the role of surprisal and retrieval cost metrics during sentence processing. While controlling for word-level predictors (word complexity, syllable length, unigram and bigram frequencies) as well as sentence-level predictors such as integration and storage costs, we find a significant effect of surprisal on first-pass reading times (higher surprisal value leads to increase in FPRT). Effect of retrieval cost was only found for a higher degree of parser parallelism. Interestingly, while surprisal has a significant effect on FPRT, storage cost (another prediction-based metric) does not. A significant effect of storage cost shows up only in total fixation time (TFT), thus indicating that these two measures perhaps capture different aspects of prediction. The study replicates previous findings that both prediction-based and memory-based metrics are required to account for processing patterns during sentence comprehension. The results also show that parser model assumptions are critical in order to draw generalizations about the utility of a metric (e.g. surprisal) across various phenomena in a language

Constraint Based Hybrid Approach to Parsing Indian Languages

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Frequency of vitamin d deficiency in multiple sclerosis patients: a cross sectional study.

Vitamin D deficiency is linked to poor treatment response in patients with Multiple S clerosis. The aim of this study is to define the frequency of Vitamin D deficiency for early detection and timely intervention leading to improved morbidity rates

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

Strong expectations cancel locality effects: evidence from Hindi.

Expectation-driven facilitation (Hale, 2001; Levy, 2008) and locality-driven retrieval difficulty (Gibson, 1998, 2000; Lewis & Vasishth, 2005) are widely recognized to be two critical factors in incremental sentence processing; there is accumulating evidence that both can influence processing difficulty. However, it is unclear whether and how expectations and memory interact. We first confirm a key prediction of the expectation account: a Hindi self-paced reading study shows that when an expectation for an upcoming part of speech is dashed, building a rarer structure consumes more processing time than building a less rare structure. This is a strong validation of the expectation-based account. In a second study, we show that when expectation is strong, i.e., when a particular verb is predicted, strong facilitation effects are seen when the appearance of the verb is delayed; however, when expectation is weak, i.e., when only the part of speech "verb" is predicted but a particular verb is not predicted, the facilitation disappears and a tendency towards a locality effect is seen. The interaction seen between expectation strength and distance shows that strong expectations cancel locality effects, and that weak expectations allow locality effects to emerge

Integration and prediction difficulty in Hindi sentence comprehension: Evidence from an eye-tracking corpus

This is the first attempt at characterizing reading difficulty in Hindi using naturally occurring sentences. We created the Potsdam-Allahabad Hindi Eyetracking Corpus by recording eye-movement data from 30 participants at the University of Allahabad, India. The target stimuli were 153 sentences selected from the beta version of the Hindi-Urdu treebank. We find that word- or low-level predictors (syllable length, unigram and bigram frequency) affect first-pass reading times, regression path duration, total reading time, and outgoing saccade length. An increase in syllable length results in longer fixations, and an increase in word unigram and bigram frequency leads to shorter fixations. Longer syllable length and higher frequency lead to longer outgoing saccades. We also find that two predictors of sentence comprehension difficulty, integration and storage cost, have an effect on reading difficulty. Integration cost (Gibson, 2000) was approximated by calculating the distance (in words) between a dependent and head; and storage cost (Gibson, 2000), which measures difficulty of maintaining predictions, was estimated by counting the number of predicted heads at each point in the sentence. We find that integration cost mainly affects outgoing saccade length, and storage cost affects total reading times and outgoing saccade length. Thus, word-level predictors have an effect in both early and late measures of reading time, while predictors of sentence comprehension difficulty tend to affect later measures. This is, to our knowledge, the first demonstration using eye-tracking that both integration and storage cost influence reading difficulty

Exploring Translation Similarities for Building a Better Sentence Aligner

Abstract. The approaches previously used for sentence alignment (sentence length, word correspondence and cognate matching) take into account different aspects of similarity between the source and the target language sentences. In this paper we discuss various aspects of similarity in translated texts that can be used for sentence alignment. We then describe a customizable method for combining several approaches that can exploit these aspects of similarity. This method also includes a novel way of using sentence length for alignment. Finally, the results of evaluation for this composite method (overall and at various stages) are presented. These results are compared with those for some previous approaches

Preverbal syntactic complexity leads to local coherence effects

The effective use of preverbal linguistic cues to make successful clause-final verbal prediction as well as robust maintenance of such predictions has been argued to be a cross-linguistic generalisation for SOV languages such as German and Japanese. In this paper, we show that native speakers of Hindi (an SOV language) falter in forming a clause-final structure in the presence of a centre-embedded relative clause with a non-canonical word order. In particular, the fallibility of the parser is illustrated by the formation of a grammatically illicit locally coherent parse during online processing. Such a parse should not be formed if the grammatically licit matrix clause final structure was being successfully formed. The formation of a locally coherent parse is further illustrated by probing various syntactic dependencies via targeted questions. We show that the parser's susceptibility to form such structures is not driven by top-down processing, rather the effect can only be explained through a bottom-up parsing approach. Further, our investigation suggests that while plausibility is essential, presence of overt agreement features might not be necessary for forming a locally coherent parse in Hindi. These results go against top-down proposals to local coherence such as lossy surprisal and are consistent with the good-enough processing model to comprehension while only partially supporting the SOPARSE account. The work highlights how top-down processing and bottom-up information interact during sentence comprehension in SOV languages – prediction suffers with increased complexity of the preverbal linguistic environment.</p

Assessing Corpus Evidence for Formal and Psycholinguistic Constraints on Nonprojectivity

Formal constraints on crossing dependencies have played a large role in research on the formal complexity of natural language grammars and parsing. Here we ask whether the apparent evidence for constraints on crossing dependencies in treebanks might arise because of independent constraints on trees, such as low arity and dependency length minimization. We address this question using two sets of experiments. In Experiment 1, we compare the distribution of formal properties of crossing dependencies, such as gap degree, between real trees and baseline trees matched for rate of crossing dependencies and various other properties. In Experiment 2, we model whether two dependencies cross, given certain psycholinguistic properties of the dependencies. We find surprisingly weak evidence for constraints originating from the mild context-sensitivity literature (gap degree and well-nestedness) beyond what can be explained by constraints on rate of crossing dependencies, topological properties of the trees, and dependency length. However, measures that have emerged from the parsing literature (e.g., edge degree, end-point crossings, and heads’ depth difference) differ strongly between real and random trees. Modeling results show that cognitive metrics relating to information locality and working-memory limitations affect whether two dependencies cross or not, but they do not fully explain the distribution of crossing dependencies in natural languages. Together these results suggest that crossing constraints are better characterized by processing pressures than by mildly context-sensitive constraints

Towards a psycholinguistically motivated dependency grammar for Hindi

Abstract The overall goal of our work is to build a dependency grammar-based human sentence processor for Hindi. As a first step towards this end, in this paper we present a dependency grammar that is motivated by psycholinguistic concerns. We describe the components of the grammar that have been automatically induced using a Hindi dependency treebank. We relate some aspects of the grammar to relevant ideas in the psycholinguistics literature. In the process, we also extract statistics and patterns for phenomena that are interesting from a processing perspective. We finally present an outline of a dependency grammar-based human sentence processor for Hindi