共查询到18条相似文献，搜索用时 171 毫秒
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FrameNet的语义类型主要用于对框架、框架元素和词元所指称的事物或现象进行概念层面的分类.文章主要通过分析各种框架关系中框架间框架元素的映射,研究了框架元素的语义本体类型的关系,发现对应的框架元素存在着语义类型相同或者语义等级关系,并以FrameNet的具体框架为例进行了论证说明. 相似文献
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框架元素语义类型的确定对于增加本体的语义信息及其建立本体之间的映射具有重要作用.探讨了利用WordNet中词汇的丰富语义等级结构,自动确定框架元素语义类型的方法,提出了同义词扩展、上位词扩展的确定思路,并引入Wu-Palmer语义相似度算法,同时考虑WordNet及FrameNet不同的语义深度,以帮助有效地选择框架元素语义类型. 相似文献
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本文论述了传统叙词表的语义关系及其所具有的局限性,对FrameNet叙词表的语义关系如词汇聚合关系、继承关系、总分关系、参照关系、因果与状态关系进行了充分揭示,并总结了其明显的语义特征。FrameNet叙词表运用本体论思想,将人工语言与自然语言、词汇与句法有机结合,为今后自动语义标注、问答系统、信息抽取和信息检索的应用奠定了基础。 相似文献
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FrameNet、WordNet、VerbNet作为语义型词典，在自然语言处理及其电子词典编纂领域得到广泛应用。这三种语义类型的词典各自从不同侧面表达词汇概念及语义关系，彼此之间互为补充，并且建立相互之间的映射，共同为语义分析提供了丰富知识资源。但这些语义型词典构建理论基础不同，形成各自明显的特征，因此文章从理论基础、组织结构、语义关系、应用范围四个层面上对这三种语义词典进行具体比较分析，明确其各自侧重点及差异，以帮助词典使用者及语言信息处理人员更好地应用。 相似文献
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主要论述基于Ontology（本体）的政府知识库的信息检索技术。该技术有别于传统的全文检索技术,采用基于本体的技术来分析处理用户提交的查询请求,分析自然语言形式问题中的词法、语法、语义等信息,识别出问题的类别,得到一些关键词,并进行扩展。还可根据本体中领域词汇的关系对关键词进行扩展,并赋予不同的权值。然后将问题类别和带权值的关键词序列提交给系统的检索引擎进行后继的处理。 相似文献
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【目的/意义】为配合汉语主题词表（生物医学农业卷）编研项目的开展,适应生物、医学、农业领域术语系统
的发展趋势,满足学科应用的实际需要。【方法/过程】本研究参考 UMLS、MeSH、SNOMED CT、AGROVOC、
CABi、中国中医药学主题词表等国内外权威术语系统构建了生物、医学、农业领域《汉表》的顶层本体语义类型框
架。【结果/结论】该框架共包括62种语义类型及其属性,并通过实证检验验证了该语义类型框架的可用性。【创新/
局限】构建的顶层本体语义类型框架能够为生物、医学、农业领域主题概念提供语义背景,在汉语主题词表（生物医
学农业卷）实际编制过程中可根据需要进一步调整。 相似文献
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基于本体的FrameNet框架之间域关系分析 总被引：1，自引：0，他引：1
介绍了FrameNet是一个巨大的框架网络体系,大部分框架之间都有一定的联系(也有个别是独立的框架不与其它有关联).框架间的关系有总分关系、域关系、继承关系、参见关系、起始关系及原因关系.在本体论的基础上,以当前正在研究的法律本体为实例,对FrameNet中框架间关系(尤其是域关系)的构建进行了详细的分析. 相似文献
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[目的/意义]针对戏曲类视频资源内容的管理难题,旨在构建一个戏曲类视频领域本体,以情节、事件、实体、动作、戏曲音乐等为核心要素,为戏曲类视频语义标注提供参考。[方法/过程]从分析戏曲类视频内容特征出发,确定本体应覆盖的范围;选择复用与参考已有的相关本体,在此基础上进行扩展,确定核心要素及其之间的关系,使用Protégé构建本体。[结果/结论]以豫剧和昆曲的两个经典剧目为例进行语义标注,通过SPARQL检索说明了标注框架的有效性。该本体可对戏曲类视频内容语义有效描述,将多方面的戏曲领域知识与视频内容进行关联,实现视频内容的结构化表达,为向不同类型的用户提供戏曲类视频内容检索提供关键基础。 相似文献
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Camilla B. Schwind 《Information processing & management》1983,19(4):223-235
This paper presents a formalism for the representation of complex semantic relations among concepts of natural language. We define a semantic algebra as a set of atomic concepts together with an ordered set of semantic relations. Semantic trees are a graphical representation of a semantic algebra (comparable to Kantorovic trees for boolean or arithmetical expressions). A semantic tree is an ordered tree with nodes labeled with relation and concept names. We generate semantic trees from natural language texts in such a way that they represent the semantic relations which hold among the concepts occurring within that text. This generation process is carried out by a transformational grammar which transforms directly natural language sentences into semantic trees. We present an example for concepts and relations within the domain of computer science where we have generated semantic trees from definition texts by means of a metalanguage for transformational grammars (a sort of metacompiler for transformational grammars). The semantic trees generated so far serve for thesaurus entries in an information retrieval system. 相似文献
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本文首先分析了传统主题检索系统的弊端,然后提出了针对这些弊端的优化技术,包括全文主题词标引、概念标引、检索式语义分析、模式匹配等相关技术,使用这些技术可以发挥主题检索语言和自然语言检索各自的优点,使传统主题检索系统成为智能化,高层次的检索系统. 相似文献
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二语词义的习得很大程度上依靠和借助母语的词义系统。本文用心理词典理论、词汇语义学的相关理论从义项的角度,探讨义项对应空缺造成的母语干扰以及汉语对掌握缅语词汇聚合和组合关系的负面影响。探讨的过程是从汉族学习者所出现的错误出发,采用对比分析的方式进行。 相似文献
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This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages. 相似文献
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Hao Zhang Frank Boons Riza Batista-Navarro 《Information processing & management》2019,56(5):1837-1848
Narratives are comprised of stories that provide insight into social processes. To facilitate the analysis of narratives in a more efficient manner, natural language processing (NLP) methods have been employed in order to automatically extract information from textual sources, e.g., newspaper articles. Existing work on automatic narrative extraction, however, has ignored the nested character of narratives. In this work, we argue that a narrative may contain multiple accounts given by different actors. Each individual account provides insight into the beliefs and desires underpinning an actor’s actions. We present a pipeline for automatically extracting accounts, consisting of NLP methods for: (1) named entity recognition, (2) event extraction, and (3) attribution extraction. Machine learning-based models for named entity recognition were trained based on a state-of-the-art neural network architecture for sequence labelling. For event extraction, we developed a hybrid approach combining the use of semantic role labelling tools, the FrameNet repository of semantic frames, and a lexicon of event nouns. Meanwhile, attribution extraction was addressed with the aid of a dependency parser and Levin’s verb classes. To facilitate the development and evaluation of these methods, we constructed a new corpus of news articles, in which named entities, events and attributions have been manually marked up following a novel annotation scheme that covers over 20 event types relating to socio-economic phenomena. Evaluation results show that relative to a baseline method underpinned solely by semantic role labelling tools, our event extraction approach optimises recall by 12.22–14.20 percentage points (reaching as high as 92.60% on one data set). Meanwhile, the use of Levin’s verb classes in attribution extraction obtains optimal performance in terms of F-score, outperforming a baseline method by 7.64–11.96 percentage points. Our proposed approach was applied on news articles focused on industrial regeneration cases. This facilitated the generation of accounts of events that are attributed to specific actors. 相似文献
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[目的/意义]资源数字化时代文献服务向知识服务方向转变,高质量的文献自动标引是文献知识服务能力提升的基础和关键,针对目前英文科技文献自动标引准确率不高的问题,提出了基于语义感知的概念遴选优化方法。[方法/过程]基于知识组织系统的自动主题标引,采用自然语言处理中的神经网络词向量技术,对概念和英文文献内容语义进行表示并进行语义感知与评估,实现概念标引结果在语义层面的遴选。该方法采用基于知识组织系统与自然语言处理技术相结合的方法,弥补了在语义层面上的不足,从而进一步降低不相关概念的影响,提高概念标引结果的准确率。[结果/结论]实验结果表明,该方法具有较好的语义感知性能,在概念遴选上有效降低了不相关概念,大大提高了标引结果的文献相关性,为科技文献资源知识化服务建设和相关研究提供有价值的参考和支持。 相似文献
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部分初中学生用汉语的思维来理解和学习英语,对英汉两种语言差异了解不够,对系动词和实义动词的区别和用法混沌不清,从而导致他们容易犯系动词和实义动词不当混用的错误,这影响了他们英语学习成绩的提高,不利于其英语思维的培养。基于此,本文对此类错误进行分析,使学生加深对英汉两种语言的差异和相关英语语法规则的了解,按照英语的语言规则做出正确的表达。教师可以通过对语法的讲解和反复的练习来提升学生的语言规则意识,调动学生的积极情感,提高学生的认知能力,促使学生英语的语言习惯和能力在自然过程中形成。 相似文献