其他
高引论文丨国内外“语义学”研究TOP1-100
后台回复“高引论文第18期”
可获取完整名单
以下排名和筛选依据“中国知网”引用数据
截止于2023年8月15日
国内高引论文（第18期）
TOP 1
语用原则、语用推理和语义演变被引频次 875
沈家煊，中国社会科学院语言研究所摘 要 当今历史语义学的主流是探讨语义演变的动因和机制,这种探讨的一个重要方面是借鉴和采纳语用学的研究成果,初步的结论是,交谈双方遵循一定的语用原则(特别是“适量准则”)是语义演变的重要动因,语用推理和推导义的“固化”是语义演变的主要机制。本文是对近年来这方面研究情况的介绍。
TOP 2
认知语言学的研究目标、原则和方法被引频次 413
文旭，西南师范大学摘 要 认知语言学是语言学中的一种新范式 ,它包含许多不同的理论和研究方法。由于认知语言学把人们的日常经验看成是语言使用的基础 ,因此 ,在许多方面它与生成语言学存在很大的差别。本文在整合认知语言学各种理论方法的基础上 ,探讨了这一认知范式的研究目标、基本原则及研究方法。
TOP 3
认知语义学
被引频次 229
王寅，苏州大学摘 要 语义理论不断翻新 ,观点各异 ,学派林立。近年来 ,认知语义学越来越得到人们的关注和认可。认知语义学家的一些主要观点 ,如语义的经验观、概念观、百科观、原型观、意象图式观、隐喻观、寓比观、象似观以及认知模型和激活理论值得介绍。语义具有动态性、可变性和不确定性。
TOP 4
框架语义学：理论与应用
被引频次 227
潘艳艳，同济大学摘 要 框架语义学是认知语言学的一个新的发展方向,本文简要介绍了框架语义学的相关理论和概念,以及框架语义学在编撰字典、分析句法、语义现象和创建语料库等方面的运用。
TOP 5
语义、认知与识解
被引频次 219
文旭，西南大学摘 要 随着认知语言学的兴起和发展,语义的研究在语言学中占据了非常重要的地位,语义学成了"灰姑娘"。本文在认知语言学的框架中,首先论述语义的认知语言学涵义,然后在此基础上分析识解的4个重要维度:详细程度、视角、勾勒、心理扫描。这些识解方式对语义的分析具有重要的意义。
TOP 6
语用标记语和语义/语用界面
被引频次 168
冯光武，广东外语外贸大学摘 要 语用与语义的纠葛从语用学诞生的那天就开始了。语义研究和语用研究的划分纷繁复杂,但归纳起来大致有:(i)它们是语言研究的不同分相,彼此独立、又相互补充;(ii)语用学是语义学的“废纸篓”,处理语义学不能处理的意义问题;(iii)语义研究的对象是语言形式,语用研究的对象是语言使用,两者互有交叉;(iv)语义研究关照句子意义、真值条件意义、命题意义和脱离语境的意义,语用研究关照说话人意义、非命题意义和具体语境中的意义。本文从语言哲学有关意义的理论说起,简要介绍众说纷纭的语义/语用界面理论,再用话语中常见的语言现象—语用标记语—来说明我们很难将语义研究和语用研究进行截然分割,最后指出语法化理论能给语义/语用界面研究带来启示。
TOP 7
多义词的认知语义框架与词典使用者的接受视野——探索多义词义项划分和释义的认知语言学模式(一)
被引频次 167
田兵，陕西师范大学摘 要 本文属于理论词典学研究的范畴。是利用认知语义学,特别是框架语义学、理想认知模型、认知语法等对词义的研究成果,以常用多义词义项的划分与释义为研究取向,探究词的认知语义框架与词典使用者的接受视野之间的关系。我们认为认识多义词的认知语义结构,区分义项间存在的多种关系,了解词典使用者的接受视野,确定义项的粒度,是解决义项划分的精粗多寡问题的前提和基础。我们考察了词的认知语义结构的相关研究,认定出义项间存在的一些基本关系。考察了词义预设的一般认知语义框架与词典使用者的特定接受视野之间的共有认知环境、词典编纂者和使用者默契协同活动中词的语义成分的共有突显。分析了词典使用者接受视野发展的动态性和义项粒度之间的反比关系。在此基础上,我们尝试提出建立义项划分和释义的认知语言学模式的设想,以期更好地实现词典释义与词典使用者接受视野的优化互动。
TOP 8
从格语法到框架网络
被引频次 164
冯志伟，教育部语言文字应用研究所
摘 要 20世纪末菲尔摩在框架语义学的基础上提出了框架网络(Fram eNet)。框架网络的目的在于研究英语中语法功能和概念结构(也就是语义结构)之间的关系,建立用于自然语言处理的词汇知识库。框架网络是格语法的最新发展,它可以更加准确地描述语言中的题元角色关系,对于语言理论研究和自然语言处理都有十分重要的意义。
TOP 9
语篇语义学与评价系统
被引频次 155
姜望琪，北京大学摘 要 本文讨论Martin的语篇语义学与评价系统之间的关系。作者首先讨论了语篇语义学的内容,指出其核心是协商、识别、连接、概念等语篇系统。接着讨论了Martin在2000,2003年的著述中正式提出的评价系统。最后,作者结合Martin和White2005年的合著《评估语言》,指出评价系统是语篇语义学的语篇系统之一。《评估语言》的出版标志评价理论已经成熟,已经成为语篇语义学研究新阶段的旗帜。
TOP 10
语义场的结构和类型
被引频次 138
周国光，华南师范大学摘 要 词汇的语义系统具有心理现实性。词汇的语义系统中存在着多种语义场。文章描述分析了现代汉语词汇系统中语义场的结构和类型 ,并讨论了相关的问题。
\ 作者分析 /
以100篇论文的所有作者为数据，高引作者（筛选）如下：
张辉、吴世雄、徐烈炯、沈家煊、文旭、陈振维、李炯英、王寅、李宝伦、潘海华、曾衍桃、吴福祥、赵忠平、白解红、方梅、石毓智、毕继万、冯光武等人。\ 单位分析 /
\ 篇名分析 /
\关键词分析 /
语义学、框架、语义、认知、语用、意义、理论、语言学、词汇、教学、语法、概念、语言、结构、元语言、释义、焦点、动词、功能、演变、模糊、原则、界面、系统、形式、心理、情态、描写等。
\ 期刊分析 /
后台回复“高引论文第18期”
可获取完整名单
以下排名和筛选依据“SSCI”引用数据
仅选Web of Science核心合集
截止于2023年8月11日
国外高引论文（第16期）
TOP 1
Simplicity: Semantics-Sensitive Integrated Matching for Picture LIbraries.
Cited frequency 1354
Wang JZ, Pennsylvania Commonwealth System of Higher Education (PCSHE)，Pennsylvania State University，Pennsylvania State University - University ParkLi J, Stanford UniversityWiederhold G, Stanford University
Abstract We present here SIMPLIcity (semantics-sensitive integrated matching for picture libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. An image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. The application of SIMPLIcity to several databases has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations.
TOP 2
Meta-analyzing left hemisphere language areas: phonology, semantics, and sentence processing.
Cited frequency 1274
Vigneau M，CEA，Centre National de la Recherche Scientifique (CNRS)Virginie Beaucousin，Université Paris-DiderotHerve Pierre-Yves，Ginesislab，BORDEAUX, FRANCEDuffau H，Universite de Montpellier，Gui de Chauliac Hosp，MONTPELLIER, FRANCEFabrice Crivello，Universite de Bordeaux，CEA，Centre National de la Recherche Scientifique (CNRS)，UDICE-French Research Universities，Communaute Universite Grenoble Alpes，Bordeaux Univ，McGill University，Universite de Caen Normandie，Universite Paris Cite，University of PalermoHoude Olivier，Universite Paris CiteNathalie Tzourio-Mazoyer，Centre National de la Recherche Scientifique (CNRS)
Abstract The advent of functional neuroimaging has allowed tremendous advances in our understanding of brain-language relationships, in addition to generating substantial empirical data on this subject in the form of thousands of activation peak coordinates reported in a decade of language studies. We performed a large-scale meta-analysis of this literature, aimed at defining the composition of the phonological, semantic, and sentence processing networks in the frontal, temporal, and inferior parietal regions of the left cerebral hemisphere. For each of these language components, activation peaks issued from relevant component-specific contrasts were submitted to a spatial clustering algorithm, which gathered activation peaks on the basis of their relative distance in the MNI space. From a sample of 730 activation peaks extracted from 129 scientific reports selected among 260, we isolated 30 activation clusters, defining the functional fields constituting three distributed networks of frontal and temporal areas and revealing the functional organization of the left hemisphere for language. The functional role of each activation cluster is discussed based on the nature of the tasks in which it was involved. This meta-analysis sheds light on several contemporary issues, notably on the fine-scale functional architecture of the inferior frontal gyrus for phonological and semantic processing, the evidence for an elementary audio-motor loop involved in both comprehension and production of syllables including the primary auditory areas and the motor mouth area, evidence of areas of overlap between phonological and semantic processing, in particular at the location of the selective human voice area that was the seat of partial overlap of the three language components, the evidence of a cortical area in the pars opercularis of the inferior frontal gyrus dedicated to syntactic processing and in the posterior part of the superior temporal gyrus a region selectively activated by sentence and text processing, and the hypothesis that different working memory perception-actions loops are identifiable for the different language components. These results argue for large-scale architecture networks rather than modular organization of language in the left hemisphere.
TOP 3
A cortical network for semantics: (de)constructing the N400.
Cited frequency 1126
Lau Ellen F，Aarhus University，Fac Hlth，AARHUS, DENMARKPhillips Colin，University of Maryland College ParkPoeppel David，Max Planck Inst Empir Aesthet，Dept Neurosci，FRANKFURT, GERMANY
Abstract Measuring event-related potentials (ERPs) has been fundamental to our understanding of how language is encoded in the brain. One particular ERP response, the N400 response, has been especially influential as an index of lexical and semantic processing. However, there remains a lack of consensus on the interpretation of this component. Resolving this issue has important consequences for neural models of language comprehension. Here we show that evidence bearing on where the N400 response is generated provides key insights into what it reflects. A neuroanatomical model of semantic processing is used as a guide to interpret the pattern of activated regions in functional MRI, magnetoencephalography and intracranial recordings that are associated with contextual semantic manipulations that lead to N400 effects.
TOP 4
A survey of content-based image retrieval with high-level semantics.
Cited frequency 999
Liu Ying，Nanyang Technological University，Sch Comp Engn，SINGAPOREDengsheng Zhang，Federation University AustraliaLu Guojun，Federation University AustraliaMa WY，Tsinghua University
Abstract In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.
TOP 5
Semantics derived automatically from language corpora contain human-like biases.
Cited frequency 895
Caliskan Aylin，Istanbul UniversityJoanna J Bryson (Bryson, Joanna J.)，University of BathNarayanan Arvind，University of Minnesota Twin Cities，MINNEAPOLIS, MN, USA
Abstract Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names. Our methods hold promise for identifying and addressing sources of bias in culture, including technology.
TOP 6
Social semantics: altruism, cooperation, mutualism, strong reciprocity and group selection.
Cited frequency 884
Stuart A West，Oxford University, Department of ZoologyAshleigh S Griffin，University of OxfordAndy Gardner，University of St AndrewsAbstract From an evolutionary perspective, social behaviours are those which have fitness consequences for both the individual that performs the behaviour, and another individual. Over the last 43 years, a huge theoretical and empirical literature has developed on this topic. However, progress is often hindered by poor communication between scientists, with different people using the same term to mean different things, or different terms to mean the same thing. This can obscure what is biologically important, and what is not. The potential for such semantic confusion is greatest with interdisciplinary research. Our aim here is to address issues of semantic confusion that have arisen with research on the problem of cooperation. In particular, we: (i) discuss confusion over the terms kin selection, mutualism, mutual benefit, cooperation, altruism, reciprocal altruism, weak altruism, altruistic punishment, strong reciprocity, group selection and direct fitness; (ii) emphasize the need to distinguish between proximate (mechanism) and ultimate (survival value) explanations of behaviours. We draw examples from all areas, but especially recent work on humans and microbes.
TOP 7
The Semantics of Predicate Logic as a Programming Language.
Cited frequency 656
Vanemden MH，University of VictoriaKowalski Robert，Imperial College LondonAbstract Sentences in first-order predicate logic can be usefully interpreted as programs. In this paper the operational and fixpoint semantics of predicate logic programs are defined, and the connections with the proof theory and model theory of logic are investigated. It is concluded that operational semantics is a part of proof theory and that fixpoint semantics is a special case of model-theoretic semantics.
TOP 8
Scale Structure, Degree Modification, and the Semantics of Gradable Predicates.
Cited frequency 532
Kennedy C，Northwestern UniversityLouise McNally，Pompeu Fabra University (UPF)Abstract In this article we develop a semantic typology of gradable predicates, with special emphasis on deverbal adjectives. We argue for the linguistic relevance of this typology by demonstrating that the distribution and interpretation of degree modifiers is sensitive to its two major classificatory parameters: (1) whether a gradable predicate is associated with what we call an open or closed scale, and (2) whether the standard of comparison for the applicability of the predicate is absolute or relative to a context. We further show that the classification of an important subclass of adjectives within the typology is largely predictable. Specifically, the scale structure of a deverbal gradable adjective correlates either with the algebraic part structure of the event denoted by its source verb or with the part structure of the entities to which the adjective applies. These correlations underscore the fact that gradability is characteristic not only of adjectives but also of verbs and nouns, and that scalar properties are shared by categorially distinct but derivationally related expressions.
TOP 9
Form and content: dissociating syntax and semantics in sentence comprehension.
Cited frequency 489
Dapretto Mirella，University of California Los Angeles，Dept Psychiat & Biobehav SciBookheimer Susan Y，University of California Los Angeles，Dept Psychiat & Biobehav Sci
Abstract The distinction between syntax (sentence form) and semantics (sentence meaning) is fundamental to our thinking about language. Whether and where this distinction is represented at the neural level is still a matter of considerable debate. In the present fMRI study, we examined the neural correlates of syntactic and semantic functions using an innovative activation paradigm specifically designed to unequivocally disentangle syntactic from lexicosemantic aspects of sentence processing. Our findings strongly indicate that a part of Broca's area (BA 44, pars opercularis) is critically implicated in processing syntactic information, whereas the lower portion of the left inferior frontal gyrus (BA 47, pars orbitalis) is selectively involved in processing the semantic aspects of a sentence.
TOP 10
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches.
Cited frequency 479
Jensen Richard，Aberystwyth UniversityQiang Shen，Aberystwyth UniversityAbstract Semantics-preserving dimensionality reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition, and signal processing. This has found successful application in tasks that involve data sets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and Web content classification. One of the many successful applications of rough set theory has been to this feature selection area. This paper reviews those techniques that preserve the underlying semantics of the data, using crisp and fuzzy rough set-based methodologies. Several approaches to feature selection based on rough set theory are experimentally compared. Additionally, a new area in feature selection, feature grouping, is highlighted and a rough set-based feature grouping technique is detailed.
\ 作者分析 /
以100篇论文的所有作者为数据，高引作者（筛选）如下：
Mcclelland JL、He Kaiming、Tulving E、Binder Jeffrey R、Desai Rutvik H、Dell Gs、Patterson K、Elman Jl、Warrington Ek、Zadeh La、Wise R、Jennings Nr、Lee Dd、Seung Hs、Gkioxari Georgia、Dollar Piotr、Girshick Ross、Snodgrass Jg、Vanderwart M、Macleod Cm、Smeulders Awm.
\ 单位分析 /
\ 篇名分析 /
\关键词分析 /
\ 期刊分析 /
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