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歧义是自然语言中的一种普遍现象,主要表现为语音歧义,词汇歧义和句法歧义.本文拟运用乔姆斯基的深、表层结构理论对英汉语言中句法歧义现象进行简要分析,找出产生歧义的原因. 相似文献
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歧义是自然语言中的一种普遍现象，主要表现为语音歧义，词汇歧义和句法歧义。本文拟运用乔姆斯基的深、表层结构理论对英汉语言中句法歧义现象进行简要分析，找出产生歧义的原因。 相似文献
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歧义是一种普遍的语言现象。文章从语义学角度,讨论英语句法歧义,对引起句法歧义的各种因素进行剖析。歧义研究对语言的理解、写作、翻译都具有实际意义,而且对语言学理论的发展也有重要作用。 相似文献
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歧义是英语中常见的一种语言现象,通常可分为词汇歧义、句法歧义和语用歧义。语言性歧义的产生是不可避免的,但只要使用得当,歧义也能产生积极的语言效果。 相似文献
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任何语言都有歧义现象,英语也不例外。我们不必极端地理解歧义的普遍性,但其在语言中的客观存在是不容忽视的。从交际效应的角度出发,可以把交际过程中出现的歧义现象分为无意歧义（unintentional ambiguity）和有意歧义（intentional ambiguity）,本文从语音、词汇、句法三个方面分析了这两种歧义现象的产生并在实例中具体探讨了如何避免无意歧义和如何巧妙运用有意歧义。 相似文献
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歧义是言语表达中普遍存在的问题,而误解、模糊、笼统、辞格运用等几种言语现象因为表义的多向性,容易与歧义混淆。本文在明确定义的基础上,从语义学、句法和语用学等方面对几种非歧义言语现象进行分析,以与歧义区别开来。 相似文献
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歧义又是语言交际中较为常见的语言现象.引起歧义的原因主要是词性的转换和句法成分.本文主要从交际效应的角度出发.结合认知语言学的相关理论,对比分析语音、词汇、结构歧义等探讨歧义这一现象;结合教学实践,选取实用性强的语料做详细分析.有助于学习者扫除语言交际中的障碍,正确理解言语交际的目的. 相似文献
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歧义是语言交际中较为常见的语言现象。引起歧义的原因主要是词性的转换和句法成分。本文主要从交际效应的角度出发。结合认知语言学的相关理论，对比分析语音、词汇、结构歧义等探讨歧义这一现象；结合教学实践，选取实用性强的语料做详细分析。有助于学习者扫除语言交际中的障碍，正确理解言语交际的目的。 相似文献
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存现句作为英汉两种语言共有的一种句法现象,具有其他句式不可替代的作用。本文分别从英汉存现句在主语、处所词、动词和名词词组方面的相似点与不同点进行分析对比,以期对教学提供有益借鉴。 相似文献
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本文运用语言迁移理论、语言输入、输出理论等,对中式英语中出现的母语负迁移现象作了分析,并得出结论:中式英语的产生是由于学习者不了解或者不能够正确运用两种语言在句法结构上的差异所造成的.最后对减少母语负迁移提出了一些建议. 相似文献
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基于企业内部与企业间因果模糊的影响因素,引入跨层次的研究方法检验因果模糊对企业创新绩效与员工服务创新能力影响的悖论。通过问卷调研106家企业,根据个体层次聚合至企业层次的变量特征模糊、关联模糊,检验两者与样本企业可持续竞争优势间的关系。结果表明：特征模糊和关联模糊对员工创新能力具有负向影响,其中互动式替代学习具有中介作用;但特征模糊和关联模糊对企业间可持续竞争优势未有正向影响。研究结论提供了从因果模糊的维度划分和结果导向两个方面的研究视角参考,并在促进员工创新能力提高、增强企业可持续竞争力方面得到企业管理者应做好保护“圈内”、干扰“圈外”,以及在大数据时代背景下企业应降低因果模糊程度等启示。 相似文献
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本文主要从修饰关系不明确引起的歧义、句子充当成分不明引起的歧义、由并列结构引起的歧义、由从属结构引起的歧义、否定范围不清引起的歧义、省略不当引起的歧义等方面分析英语语法歧义根源,并从改变句子中词语排列的顺序、改变词语、变换句式、成分补充、设置具体语境等方面提出避免英语语法歧义的方法。 相似文献
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《Information processing & management》2023,60(5):103469
This paper focuses on extracting temporal and parent–child relationships between news events in social news. Previous methods have proved that syntactic features are valid. However, most previous methods directly use the static outcomes parsed by syntactic parsing tools, but task-irrelevant or erroneous parses will inevitably degrade the performance of the model. In addition, many implicit higher-order connections that are directly related and critical to tasks are not explicitly exploited. In this paper, we propose a novel syntax-based dynamic latent graph model (SDLG) for this task. Specifically, we first apply a syntactic type-enhanced attention mechanism to assign different weights to different connections in the parsing results, which helps to filter out noisy connections and better fuse the information in the syntactic structures. Next, we introduce a dynamic event pair-aware induction graph to mine the task-related latent connections. It constructs a potential attention matrix to complement and correct the supervised syntactic features, using the semantics of the event pairs as a guide. Finally, the latent graph, together with the syntactic information, is fed into the graph convolutional network to obtain an improved representation of the event to complete relational reasoning. We have conducted extensive experiments on four public benchmarks, MATRES, TCR, HiEve and TB-Dense. The results show that our model outperforms the state-of-the-art model by 0.4%, 1.5%, 3.0% and 1.3% in F1 scores on the four datasets, respectively. Finally, we provide detailed analyses to show the effectiveness of each proposed component. 相似文献
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This paper describes an automated procedure for morphosyntactic analysis of medical English based on the recognition of terminal suffixes as syntactic markers. In addition, certain suffixes convey semantic information about a semantic category to which a word may belong.The output of the morphosyntactic analysis provides the input for subsequent syntactic and semantic analysis on the sentence level. 相似文献
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Lanting Fang Luu Anh Tuan Siu Cheung Hui Lenan Wu 《Information processing & management》2018,54(2):184-202
With the popularity of online educational platforms, English learners can learn and practice no matter where they are and what they do. English grammar is one of the important components in learning English. To learn English grammar effectively, it requires students to practice questions containing focused grammar knowledge. In this paper, we study a novel problem of retrieving English grammar questions with similar grammatical focus. Since the grammatical focus similarity is different from textual similarity or sentence syntactic similarity, existing approaches cannot be applied directly to our problem. To address this problem, we propose a syntactic based approach for English grammar question retrieval which can retrieve related grammar questions with similar grammatical focus effectively. In the proposed syntactic based approach, we first propose a new syntactic tree, namely parse-key tree, to capture English grammar questions’ grammatical focus. Next, we propose two kernel functions, namely relaxed tree kernel and part-of-speech order kernel, to compute the similarity between two parse-key trees of the query and grammar questions in the collection. Then, the retrieved grammar questions are ranked according to the similarity between the parse-key trees. In addition, if a query is submitted together with answer choices, conceptual similarity and textual similarity are also incorporated to further improve the retrieval accuracy. The performance results have shown that our proposed approach outperforms the state-of-the-art methods based on statistical analysis and syntactic analysis. 相似文献
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《Information processing & management》2022,59(3):102891
Syntax parse trees are a method of representing sentence structure and are often used to provide models with syntax information and enhance downstream task performance. Because grammar and syntax are inherently linked, the incorporation of syntax parse trees in GEC is a natural solution. In this work, we present a method of incorporating syntax parse trees for Grammatical Error Correction (GEC). Building off a strong sequence-to-sequence Transformer baseline, we present a unified parse integration method for GEC that allows for the use of both dependency and constituency parse trees, as well as their combination - a syntactic graph. Specifically, on the sentence encoder, we propose a graph encoder that can encode dependency trees and constituent trees at the same time, yielding two representations for terminal nodes (i.e., the token of the sentence) and non-terminal nodes. We next use two cross-attentions (NT-Cross-Attention and T-Cross-Attention) to aggregate these source syntactic representations to the target side for final corrections prediction. In addition to evaluating our models on the popular CoNLL-2014 Shared Task and JFLEG GEC benchmarks, we affirm the effectiveness of our proposed method by testing both varying levels of parsing quality and exploring the use of both parsing formalisms. With further empirical exploration and analysis to identify the source of improvement, we found that rich syntax information provided clear clues for GEC; a syntactic graph composed of multiple syntactic parse trees can effectively compensate for the limited quality and insufficient error correction capability of a single syntactic parse tree. 相似文献
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知识特征与知识传递媒体的选择 总被引：3，自引：0，他引：3
知识传递是知识创造和应用的一个重要基础,本文从知识特征入手,分析根据知识特征如何选择合适的知识传递媒体。首先,本文定义了知识歧义性,论证了知识歧义性与性息歧义性的本质一致,并指出减少歧义性的根本途径都是组织学习;然后论证知识歧义性是选择知识传递媒体的重要影响因素,并进一步指出知识的隐性特征是选择知识传递媒体的重要影响因素;然后引入信息富裕和媒体富裕的概念,得到知识特征与媒体富裕匹配规律,即知识的隐性特征越强,所需的媒体富裕度越高,最后用已有的实证研究证明了该规律的正确性。 相似文献
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文本挖掘是数据挖掘技术的一个重要方面,本文根据句法规则的特征,利用文本挖掘技术,提出基于句法规则的文本知识挖掘设计模型,从数据准备、句法规则构造、文本预处理、文本知识挖掘、挖掘结果评价等方面对工作原理进行了分析,重点阐述了句法规则的构造过程,最后通过实验验证了该模型,该设计对实现文本知识的智能化挖掘具有一定的研究意义和应用价值。 相似文献