A scalable and adaptive method for finding semantically equivalent cue words of uncertainty
A scalable and adaptive method for finding semantically equivalent cue words of uncertainty
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DOI：
10.1016/j.joi.2017.12.004
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发表时间：
2018-02-01
影响因子：
3.7
通讯作者：
Heo, Go Eun
中科院分区：
文献类型：
--
作者：
Chen, Chaomei;Song, Min;Heo, Go Eun
由于新的发现、不同的解释和新的观点，科学知识不断地发生各种变化。理解与科学探究的各个阶段相关的不确定性是科学家领域专长的一个组成部分，也是他们科学元知识的核心。尽管人们对计算语言学等领域的兴趣越来越大，但系统地描述和跟踪科学主张的认识状态及其在科学学科中的演变仍然是一个挑战。我们提出了一个统一的框架，明确和隐含的科学出版物中传达的不确定性的研究。该框架旨在适应广泛的不确定性类型，从猜测到不一致和争议。我们引入了一个可扩展的和自适应的方法来识别语义上等价的线索的不确定性在不同的研究领域，并适应个人分析师的独特观点。我们演示了如何使用新的方法来扩展一个小的种子列表的不确定性线索词和扩展的候选线索词的有效性进行验证。我们将原始和扩展的不确定性线索词的混合物可视化，以揭示不确定性表达的多样性。这些线索词为研究科学断言中的不确定性提供了新的资源。(C)2017爱思唯尔有限公司版权所有
Scientific knowledge is constantly subject to a variety of changes due to new discoveries, alternative interpretations, and fresh perspectives. Understanding uncertainties associated with various stages of scientific inquiries is an integral part of scientists' domain expertise and it serves as the core of their meta-knowledge of science. Despite the growing interest in areas such as computational linguistics, systematically characterizing and tracking the epistemic status of scientific claims and their evolution in scientific disciplines remains a challenge. We present a unifying framework for the study of uncertainties explicitly and implicitly conveyed in scientific publications. The framework aims to accommodate a wide range of uncertainty types, from speculations to inconsistencies and controversies. We introduce a scalable and adaptive method to recognize semantically equivalent cues of uncertainty across different fields of research and accommodate individual analysts' unique perspectives. We demonstrate how the new method can be used to expand a small seed list of uncertainty cue words and how the validity of the expanded candidate cue words is verified. We visualize the mixture of the original and expanded uncertainty cue words to reveal the diversity of expressions of uncertainty. These cue words offer a novel resource for the study of uncertainty in scientific assertions. (C) 2017 Elsevier Ltd. All rights reserved.