6,714 research outputs found
Response Functions to Critical Shocks in Social Sciences: An Empirical and Numerical Study
We show that, provided one focuses on properly selected episodes, one can
apply to the social sciences the same observational strategy that has proved
successful in natural sciences such as astrophysics or geodynamics. For
instance, in order to probe the cohesion of a policy, one can, in different
countries, study the reactions to some huge and sudden exogenous shocks, which
we call Dirac shocks. This approach naturally leads to the notion of structural
(as opposed or complementary to temporal) forecast. Although structural
predictions are by far the most common way to test theories in the natural
sciences, they have been much less used in the social sciences. The Dirac shock
approach opens the way to testing structural predictions in the social
sciences. The examples reported here suggest that critical events are able to
reveal pre-existing ``cracks'' because they probe the social cohesion which is
an indicator and predictor of future evolution of the system, and in some cases
foreshadows a bifurcation. We complement our empirical work with numerical
simulations of the response function (``damage spreading'') to Dirac shocks in
the Sznajd model of consensus build-up. We quantify the slow relaxation of the
difference between perturbed and unperturbed systems, the conditions under
which the consensus is modified by the shock and the large variability from one
realization to another
An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms
This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their causeโeffect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea
Teaching and learning special relativity theory in secondary and lower undergraduate education: A literature review
This review presents an overview and analysis of the body of research on
special relativity theory (SRT) education at the secondary and lower
undergraduate level. There is currently a growing international interest in
implementing SRT in pre-university education as an introduction to modern
physics. For this reason, insights into learning opportunities and challenges
in SRT education are needed. The field of research in SRT education is still at
an early stage, especially at the level of secondary education, and there is a
shortage of empirical evaluation of learning outcomes. In order to guide future
research directions, there is a need for an overview and synthesis of the
results reported so far. We have selected 40 articles and categorized them
according to reported learning difficulties, teaching approaches, and research
tools. Analysis shows that students at all educational levels experience
learning difficulties with the use of frames of reference, the postulates of
SRT, and relativistic effects. In the reported teaching sequences,
instructional materials, and learning activities, these difficulties are
approached from different angles. Some teaching approaches focus on thought
experiments to express conceptual features of SRT, while others use virtual
environments to provide realistic visualization of relativistic effects. From
the reported teaching approaches, three learning objectives can be identified:
to foster conceptual understanding, to foster understanding of the history and
philosophy of science, and to gain motivation and confidence toward SRT and
physics in general. In order to quantitatively compare learning outcomes of
different teaching strategies, a more thorough evaluation of assessment tools
is required
One, no one and one hundred thousand events: Defining and processing events in an inter-disciplinary perspective
We present an overview of event definition and processing spanning 25 years of research in NLP. We first provide linguistic background to the notion of event, and then present past attempts to formalize this concept in annotation standards to foster the development of benchmarks for event extraction systems. This ranges from MUC-3 in 1991 to the Time and Space Track challenge at SemEval 2015. Besides, we shed light on other disciplines in which the notion of event plays a crucial role, with a focus on the historical domain. Our goal is to provide a comprehensive study on event definitions and investigate which potential past efforts in the NLP community may have in a different research domain. We present the results of a questionnaire, where the notion of event for historians is put in relation to the NLP perspective
The influence of gender and cultural values on savoring in Korean undergraduates
The present study investigated antecedents of savoring beliefs and responses in a sample of South Korean college students. Historically, Korea has been strongly influenced by Chinese Confucianism, which emphasizes not only gender-role differentiation and patriarchal norms, but also the dampening of emotions as a culturally appropriate style of positive emotional regulation. We hypothesized that Korean females, relative to males, would reject traditional Asian cultural values in order to gain more empowerment, and would, as a result, report a greater capacity to savor positive experience. Confirming the hypotheses, Korean women, compared to men, reported stronger disagreement with traditional Asian values, greater overall savoring ability, greater capacity for cognitive elaboration, and less use of dampening and greater use of amplifying as savoring responses to positive events. Path analyses supported our hypothesized mediational model in which Korean women, relative to men, more strongly rejected traditional Asian values, which predicted less dampening (but only marginally greater amplifying). We conclude that among young Korean adults: (a) savoring is a relevant concept; (b) traditional Asian values tend to promote dampening of positive emotions; and (c) women more strongly reject traditional cultural values, tend to engage in less dampening and greater amplifying, and perceive greater savoring capacity, relative to men
Past-as-Past in counterfactual desire reports: a view from Japanese
The semantic contribution of Fake Past in counterfactual expressions has been actively debated in recent semantic literature. This study deepens our current understanding of this natural language phenomenon by digging into the behavior of Past tense in Japanese counterfactual desire reports. We show that ย the Past-as-Past approach to Fake Past makes correct predictions about its semantic behavior
์ฝ๋ฌผ ๊ฐ์๋ฅผ ์ํ ๋น์ ํ ํ ์คํธ ๋ด ์์ ์ ๋ณด ์ถ์ถ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์ตํฉ๊ณผํ๊ธฐ์ ๋ํ์ ์์ฉ๋ฐ์ด์ค๊ณตํ๊ณผ, 2023. 2. ์ดํ๊ธฐ.Pharmacovigilance is a scientific activity to detect, evaluate and understand the occurrence of adverse drug events or other problems related to drug safety. However, concerns have been raised over the quality of drug safety information for pharmacovigilance, and there is also a need to secure a new data source to acquire drug safety information. On the other hand, the rise of pre-trained language models
based on a transformer architecture has accelerated the application of natural language processing (NLP) techniques in diverse domains. In this context, I tried to define two problems in pharmacovigilance as an NLP task and provide baseline models for the defined tasks: 1) extracting comprehensive drug safety information from adverse drug events narratives reported through a spontaneous reporting system (SRS) and 2) extracting drug-food interaction information from abstracts of biomedical articles. I developed annotation guidelines and performed manual annotation, demonstrating that strong NLP models can be trained to extracted clinical information from unstructrued free-texts by fine-tuning transformer-based language models on a high-quality annotated corpus. Finally, I discuss issues to consider when when developing annotation guidelines for extracting clinical information related to pharmacovigilance. The annotated corpora and the NLP models in this dissertation can streamline pharmacovigilance activities by enhancing the data quality of reported drug safety information and expanding the data sources.์ฝ๋ฌผ ๊ฐ์๋ ์ฝ๋ฌผ ๋ถ์์ฉ ๋๋ ์ฝ๋ฌผ ์์ ์ฑ๊ณผ ๊ด๋ จ๋ ๋ฌธ์ ์ ๋ฐ์์ ๊ฐ์ง, ํ๊ฐ ๋ฐ ์ดํดํ๊ธฐ ์ํ ๊ณผํ์ ํ๋์ด๋ค. ๊ทธ๋ฌ๋ ์ฝ๋ฌผ ๊ฐ์์ ์ฌ์ฉ๋๋ ์์ฝํ ์์ ์ฑ ์ ๋ณด์ ๋ณด๊ณ ํ์ง์ ๋ํ ์ฐ๋ ค๊ฐ ๊พธ์คํ ์ ๊ธฐ๋์์ผ๋ฉฐ, ํด๋น ๋ณด๊ณ ํ์ง์ ๋์ด๊ธฐ ์ํด์๋ ์์ ์ฑ ์ ๋ณด๋ฅผ ํ๋ณดํ ์๋ก์ด ์๋ฃ์์ด ํ์ํ๋ค. ํํธ ํธ๋์คํฌ๋จธ ์ํคํ
์ฒ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ฌ์ ํ๋ จ ์ธ์ด๋ชจ๋ธ์ด ๋ฑ์ฅํ๋ฉด์ ๋ค์ํ ๋๋ฉ์ธ์์ ์์ฐ์ด์ฒ๋ฆฌ ๊ธฐ์ ์ ์ฉ์ด ๊ฐ์ํ๋์๋ค. ์ด๋ฌํ ๋งฅ๋ฝ์์ ๋ณธ ํ์ ๋
ผ๋ฌธ์์๋ ์ฝ๋ฌผ ๊ฐ์๋ฅผ ์ํ ๋ค์ 2๊ฐ์ง ์ ๋ณด ์ถ์ถ ๋ฌธ์ ๋ฅผ ์์ฐ์ด์ฒ๋ฆฌ ๋ฌธ์ ํํ๋ก ์ ์ํ๊ณ ๊ด๋ จ ๊ธฐ์ค ๋ชจ๋ธ์ ๊ฐ๋ฐํ์๋ค: 1) ์๋์ ์ฝ๋ฌผ ๊ฐ์ ์ฒด๊ณ์ ๋ณด๊ณ ๋ ์ด์์ฌ๋ก ์์ ์๋ฃ์์ ํฌ๊ด์ ์ธ ์ฝ๋ฌผ ์์ ์ฑ ์ ๋ณด๋ฅผ ์ถ์ถํ๋ค. 2) ์๋ฌธ ์์ฝํ ๋
ผ๋ฌธ ์ด๋ก์์ ์ฝ๋ฌผ-์ํ ์ํธ์์ฉ ์ ๋ณด๋ฅผ ์ถ์ถํ๋ค. ์ด๋ฅผ ์ํด ์์ ์ฑ ์ ๋ณด ์ถ์ถ์ ์ํ ์ด๋
ธํ
์ด์
๊ฐ์ด๋๋ผ์ธ์ ๊ฐ๋ฐํ๊ณ ์์์
์ผ๋ก ์ด๋
ธํ
์ด์
์ ์ํํ์๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก ๊ณ ํ์ง์ ์์ฐ์ด ํ์ต๋ฐ์ดํฐ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ฌ์ ํ์ต ์ธ์ด๋ชจ๋ธ์ ๋ฏธ์ธ ์กฐ์ ํจ์ผ๋ก์จ ๋น์ ํ ํ
์คํธ์์ ์์ ์ ๋ณด๋ฅผ ์ถ์ถํ๋ ๊ฐ๋ ฅํ ์์ฐ์ด์ฒ๋ฆฌ ๋ชจ๋ธ ๊ฐ๋ฐ์ด ๊ฐ๋ฅํจ์ ํ์ธํ์๋ค. ๋ง์ง๋ง์ผ๋ก ๋ณธ ํ์ ๋
ผ๋ฌธ์์๋ ์ฝ๋ฌผ๊ฐ์์ ๊ด๋ จ๋์์ ์ ๋ณด ์ถ์ถ์ ์ํ ์ด๋
ธํ
์ด์
๊ฐ์ด๋๋ผ์ธ์ ๊ฐ๋ฐํ ๋ ๊ณ ๋ คํด์ผ ํ ์ฃผ์ ์ฌํญ์ ๋ํด ๋
ผ์ํ์๋ค. ๋ณธ ํ์ ๋
ผ๋ฌธ์์ ์๊ฐํ ์์ฐ์ด ํ์ต๋ฐ์ดํฐ์ ์์ฐ์ด์ฒ๋ฆฌ ๋ชจ๋ธ์ ์ฝ๋ฌผ ์์ ์ฑ ์ ๋ณด์ ๋ณด๊ณ ํ์ง์ ํฅ์์ํค๊ณ ์๋ฃ์์ ํ์ฅํ์ฌ ์ฝ๋ฌผ ๊ฐ์ ํ๋์ ๋ณด์กฐํ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.Chapter 1 1
1.1 Contributions of this dissertation 2
1.2 Overview of this dissertation 2
1.3 Other works 3
Chapter 2 4
2.1 Pharmacovigilance 4
2.2 Biomedical NLP for pharmacovigilance 6
2.2.1 Pre-trained language models 6
2.2.2 Corpora to extract clinical information for pharmacovigilance 9
Chapter 3 11
3.1 Motivation 12
3.2 Proposed Methods 14
3.2.1 Data source and text corpus 15
3.2.2 Annotation of ADE narratives 16
3.2.3 Quality control of annotation 17
3.2.4 Pretraining KAERS-BERT 18
3.2.6 Named entity recognition 20
3.2.7 Entity label classification and sentence extraction 21
3.2.8 Relation extraction 21
3.2.9 Model evaluation 22
3.2.10 Ablation experiment 23
3.3 Results 24
3.3.1 Annotated ICSRs 24
3.3.2 Corpus statistics 26
3.3.3 Performance of NLP models to extract drug safety information 28
3.3.4 Ablation experiment 31
3.4 Discussion 33
3.5 Conclusion 38
Chapter 4 39
4.1 Motivation 39
4.2 Proposed Methods 43
4.2.1 Data source 44
4.2.2 Annotation 45
4.2.3 Quality control of annotation 49
4.2.4 Baseline model development 49
4.3 Results 50
4.3.1 Corpus statistics 50
4.3.2 Annotation Quality 54
4.3.3 Performance of baseline models 55
4.3.4 Qualitative error analysis 56
4.4 Discussion 59
4.5 Conclusion 63
Chapter 5 64
5.1 Issues around defining a word entity 64
5.2 Issues around defining a relation between word entities 66
5.3 Issues around defining entity labels 68
5.4 Issues around selecting and preprocessing annotated documents 68
Chapter 6 71
6.1 Dissertation summary 71
6.2 Limitation and future works 72
6.2.1 Development of end-to-end information extraction models from free-texts to database based on existing structured information 72
6.2.2 Application of in-context learning framework in clinical information extraction 74
Chapter 7 76
7.1 Annotation Guideline for "Extraction of Comprehensive Drug Safety Information from Adverse Event Narratives Reported through Spontaneous Reporting System" 76
7.2 Annotation Guideline for "Extraction of Drug-Food Interactions from the Abtracts of Biomedical Articles" 100๋ฐ
Forecasting Future World Events with Neural Networks
Forecasting future world events is a challenging but valuable task. Forecasts
of climate, geopolitical conflict, pandemics and economic indicators help shape
policy and decision making. In these domains, the judgment of expert humans
contributes to the best forecasts. Given advances in language modeling, can
these forecasts be automated? To this end, we introduce Autocast, a dataset
containing thousands of forecasting questions and an accompanying news corpus.
Questions are taken from forecasting tournaments, ensuring high quality,
real-world importance, and diversity. The news corpus is organized by date,
allowing us to precisely simulate the conditions under which humans made past
forecasts (avoiding leakage from the future). Motivated by the difficulty of
forecasting numbers across orders of magnitude (e.g. global cases of COVID-19
in 2022), we also curate IntervalQA, a dataset of numerical questions and
metrics for calibration. We test language models on our forecasting task and
find that performance is far below a human expert baseline. However,
performance improves with increased model size and incorporation of relevant
information from the news corpus. In sum, Autocast poses a novel challenge for
large language models and improved performance could bring large practical
benefits.Comment: NeurIPS 2022; our dataset is available at
https://github.com/andyzoujm/autocas
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