scholarly journals Quantification in event semantics: generalized quantifiers vs. sub-events

2018 ◽  
Vol 60 ◽  
pp. 39-53
Author(s):  
Sascha Alexeyenko
Keyword(s):  
Generalized Quantifiers ◽  
Existential Quantifier ◽  
Event Semantics ◽  
Event Type

The goal of this paper is to evaluate two approaches to quantification in event semantics,namely the analysis of quantificational DPs in terms of generalized quantifiers andthe analysis proposed in Schein (1993) according to which quantifiers over individuals containan existential quantifier over sub-events in their scope. Both analyses capture the fact that theevent quantifier always takes scope under quantifiers over individuals (the Event Type Principlein Landman (2000)), but the sub-events analysis has also been argued to be able to accountfor some further data, namely for adverbs qualifying ‘ensemble’ events and for mixed cumulative/distributive readings. This paper shows that the sub-events analysis also provides a betteraccount of the Event Type Principle if a broader range of data is considered, including caseswith non-existential quantifiers over events: unlike the generalized quantifiers analysis, it cansuccessfully account for the interpretation of indefinites in bare habituals and sentences thatcontain overt adverbs of quantification.Keywords: quantification, event semantics, generic quantifier, habituals, Q-adverbs.

Head Impact Biomechanics in Youth Flag Football: A Prospective Cohort Study

2021 ◽  
pp. 036354652110266
Author(s):  
Landon B. Lempke ◽  
Rachel S. Johnson ◽  
Rachel K. Le ◽  
Melissa N. Anderson ◽  
Julianne D. Schmidt ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Acceleration ◽  
Cross Sectional Study ◽  
Head Impact ◽  
Rotational Acceleration ◽  
Event Type ◽  
Level Of Evidence ◽  
Cross Sectional ◽  
Head Impact Biomechanics ◽  
Impact Biomechanics

Background: Youth flag football participation has rapidly grown and is a potentially safer alternative to tackle football. However, limited research has quantitatively assessed youth flag football head impact biomechanics. Purpose: To describe head impact biomechanics outcomes in youth flag football and explore factors associated with head impact magnitudes. Study Design: Cross-sectional study; Level of evidence, 3. Methods: We monitored 52 player-seasons among 48 male flag football players (mean ± SD; age, 9.4 ± 1.1 years; height, 138.6 ± 9.5 cm; mass, 34.7 ± 9.2 kg) across 3 seasons using head impact sensors during practices and games. Sensors recorded head impact frequencies, peak linear ( g) and rotational (rad/s2) acceleration, and estimated impact location. Impact rates (IRs) were calculated as 1 impact per 10 player-exposures; IR ratios (IRRs) were used to compare season, event type, and age group IRs; and 95% CIs were calculated for IRs and IRRs. Weekly and seasonal cumulative head impact frequencies and magnitudes were calculated. Mixed-model regression models examined the association between player characteristics, event type, and seasons and peak linear and rotational accelerations. Results: A total of 429 head impacts from 604 exposures occurred across the study period (IR, 7.10; 95% CI, 4.81-10.50). Weekly and seasonal cumulative median head impact frequencies were 1.00 (range, 0-2.63) and 7.50 (range, 0-21.00), respectively. The most frequent estimated head impact locations were the skull base (n = 96; 22.4%), top of the head (n = 74; 17.2%), and back of the head (n = 66; 15.4%). The combined event type IRs differed among the 3 seasons (IRR range, 1.45-2.68). Games produced greater IRs (IRR, 1.24; 95% CI, 1.01-1.53) and peak linear acceleration (mean difference, 5.69 g; P = .008) than did practices. Older players demonstrated greater combined event–type IRs (IRR, 1.46; 95% CI, 1.12-1.90) and increased head impact magnitudes than did younger players, with every 1-year age increase associated with a 3.78 g and 602.81-rad/s2 increase in peak linear and rotational acceleration magnitude, respectively ( P≤ .005). Conclusion: Head IRs and magnitudes varied across seasons, thus highlighting multiple season and cohort data are valuable when providing estimates. Head IRs were relatively low across seasons, while linear and rotational acceleration magnitudes were relatively high.

Elucidation of a telic infinitive

2001 ◽  
Vol 37 (2) ◽  
pp. 313-337 ◽  
Author(s):  
MATTHEW WHELPTON
Keyword(s):  
Argument Structure ◽  
Event Semantics

In this paper, I consider the semantics of a modifier infinitive in English, related to the more widely discussed Rationale Clause (see especially Faraci 1974, Jones 1991). I argue that the semantics of this infinitive (a Telic Clause) derives from the properties of the predicate which heads it (TELOS). I characterize TELOS, within a Davidsonian event semantics, as a pure relation between events and argue against the view that the word only, which often prefaces the Telic Clause, is in fact the head of the construction. I explore the conditions on reference which apply to TELOS. As well as offering an account of a little-discussed construction, therefore, this paper is intended as a contribution to our understanding of the place of argument structure in event semantics.

An Evaluation of Patient Safety Event Report Categories Using Unsupervised Topic Modeling

2015 ◽  
Vol 54 (04) ◽  
pp. 338-345 ◽  
Author(s):  
A. Fong ◽  
R. Ratwani
Keyword(s):  
Patient Safety ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Topic Modeling ◽  
Free Text ◽  
Event Data ◽  
Event Type ◽  
Modeling Approach ◽  
Safety Event

SummaryObjective: Patient safety event data repositories have the potential to dramatically improve safety if analyzed and leveraged appropriately. These safety event reports often consist of both structured data, such as general event type categories, and unstructured data, such as free text descriptions of the event. Analyzing these data, particularly the rich free text narratives, can be challenging, especially with tens of thousands of reports. To overcome the resource intensive manual review process of the free text descriptions, we demonstrate the effectiveness of using an unsupervised natural language processing approach.Methods: An unsupervised natural language processing technique, called topic modeling, was applied to a large repository of patient safety event data to identify topics, or themes, from the free text descriptions of the data. Entropy measures were used to evaluate and compare these topics to the general event type categories that were originally assigned by the event reporter.Results: Measures of entropy demonstrated that some topics generated from the un-supervised modeling approach aligned with the clinical general event type categories that were originally selected by the individual entering the report. Importantly, several new latent topics emerged that were not originally identified. The new topics provide additional insights into the patient safety event data that would not otherwise easily be detected.Conclusion: The topic modeling approach provides a method to identify topics or themes that may not be immediately apparent and has the potential to allow for automatic reclassification of events that are ambiguously classified by the event reporter.

Event Mining Through Clustering

2014 ◽  
Vol 23 (1) ◽  
pp. 59-73
Author(s):  
E. Umamaheswari ◽  
T.V. Geetha
Keyword(s):  
Clustering Algorithm ◽  
Semantic Representation ◽  
Clustering Algorithms ◽  
Event Semantics ◽  
Information Retrieval Evaluation ◽  
Event Mining ◽  
Unique Word ◽  
Event Clustering ◽  
Cluster Efficiency ◽  
Single Cluster

AbstractTraditional document clustering algorithms consider text-based features such as unique word count, concept count, etc. to cluster documents. Meanwhile, event mining is the extraction of specific events, their related sub-events, and the associated semantic relations from documents. This work discusses an approach to event mining through clustering. The Universal Networking Language (UNL)-based subgraph, a semantic representation of the document, is used as the input for clustering. Our research focuses on exploring the use of three different feature sets for event clustering and comparing the approaches used for specific event mining. In our previous work, the clustering algorithm used UNL-based event semantics to represent event context for clustering. However, this approach resulted in different events with similar semantics being clustered together. Hence, instead of considering only UNL event semantics, we considered assigning additional weights to similarity between event contexts with event-related attributes such as time, place, and persons. Although we get specific events in a single cluster, sub-events related to the specific events are not necessarily in a single cluster. Therefore, to improve our cluster efficiency, connective terms between two sentences and their representation as UNL subgraphs were also considered for similarity determination. By combining UNL semantics, event-specific arguments similarity, and connective term concepts between sentences, we were able to obtain clusters for specific events and their sub-events. We have used 112 000 Tamil documents from the Forum for Information Retrieval Evaluation data corpus and achieved good results. We have also compared our approach with the previous state-of-the-art approach for Router-RCV1 corpus and achieved 30% improvements in precision.

scholarly journals A fuzzy syllogistic reasoning schema for generalized quantifiers

2014 ◽  
Vol 234 ◽  
pp. 79-96 ◽  
Author(s):  
M. Pereira-Fariña ◽  
Juan C. Vidal ◽  
F. Dĺaz-Hermida ◽  
A. Bugarĺn
Keyword(s):  
Syllogistic Reasoning ◽  
Generalized Quantifiers

scholarly journals Enhancement of stratospheric aerosols after solar proton event

1996 ◽  
Vol 14 (11) ◽  
pp. 1119-1123 ◽  
Author(s):  
O. I. Shumilov ◽  
E. A. Kasatkina ◽  
K. Henriksen ◽  
E. V. Vashenyuk
Keyword(s):  
Considerable Increase ◽  
Ground Level ◽  
Solar Proton ◽  
Stratospheric Aerosol ◽  
Nucleation Mechanism ◽  
Solar Proton Event ◽  
Proton Event ◽  
Aerosol Layer ◽  
Event Type ◽  
Lidar Measurements

Abstract. The lidar measurements at Verhnetulomski observatory (68.6°N, 31.8°E) at Kola peninsula detected a considerable increase of stratospheric aerosol concentration after the solar proton event of GLE (ground level event) type on the 16/02/84. This increase was located at precisely the same altitude range where the energetic solar protons lost their energy in the atmosphere. The aerosol layer formed precipitated quickly (1–2 km per day) during 18, 19, and 20 February 1984, and the increase of R(H) (backscattering ratio) at 17 km altitude reached 40% on 20/02/84. We present the model calculation of CN (condensation nuclei) altitude distribution on the basis of an ion-nucleation mechanism, taking into account the experimental energy distribution of incident solar protons. The meteorological situation during the event was also investigated.

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