隐喻机器理解的若干关键问题研究
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摘要
语言理解是人类智能的一项重要特性,隐喻作为自然语言中的一种普遍现象,体现了人类重要的思维方式。隐喻是人类概念系统的一部分,我们的许多思考方式以及所体验的事物对认知活动所造成的影响,实际上都是隐喻的某种表征。随着隐喻认知研究不断深入,隐喻理解的认知机制及其计算化研究显得十分迫切,尤其是在提高自然语言理解的效果方面更是如此。
本文从当前隐喻理论的研究热点入手,在总结现有隐喻计算模型的基础上,提出隐喻计算化研究需要解决的关键问题,就隐喻语言的形式化描述与分类识别、隐喻理解中的知识表示问题、隐喻的映射问题以及认知主体的主观因素等方面进行重点探讨。
在隐喻语言的形式化描述与分类识别问题上,根据语言学领域对隐喻语言的定义,将隐喻语言划分为指称型隐喻和搭配型隐喻两大类。以依存语法理论为句子形式化的基础,将句子描述为依存结构树,并通过对大量隐喻句依存结构树的详细分析和归类,得出隐喻句的各种依存模式,最终提出一种高效的树模式匹配算法,快速有效地识别句子依存结构树中可能存在的隐喻单元位置。针对指称型隐喻的判别,给出一种基于上下位语义关系的指称可适度量化公式:当两个词语的指称可适度低于设定的阈值时判定为存在指称隐喻;针对搭配型隐喻,提出一种基于实例和语义知识库相结合的搭配可适度量化方法:当两个词语的搭配可适度低于设定的阈值时判定存在搭配隐喻。最后根据隐喻依存模式提供的规则判断具体的隐喻类别和隐喻角色标注。
在隐喻的理解问题上,结合语用学和认知语言学在隐喻理解方面的各类观点,特别是语用学对隐喻理解过程的论述和认知语言学关于隐喻表征映射方面的研究成果,采用本体技术作为概念知识的表示方法,依据概念合成理论框架,将隐喻映射问题转化为由本体表示的心理空间之间的概念映射问题。论文把概念合成框架中的类属空间表示为由知网义原体系扩展构成的通用本体,把输入空间看作是一个在线的知识结构,同样由本体模型来描述。输入空间本体通过隐喻依存结构树中各隐喻角色来获取,其中源域角色(Met_S)和目标域角色(Met_T)经过词汇概念本体获取对应的概念,并从句子的语义角色分析和语义知识库获取相关的论元角色类型。根据三角规则和修整规则对两个输入空间本体中的概念进行映射匹配,并根据映射的整合度对各个匹配进行评估,进而形成合成空间来解释隐喻。
隐喻的理解涉及认知主体的主观认知状态,包括隐喻话题的世界知识、认知主体的信念、意图等。以往的隐喻计算研究忽略了隐喻理解过程中的这一特性,强调客观知识的作用,因而更多关注于隐喻的语言特征和世界知识的表示。基于此,本文在对隐喻理解过程进行逻辑刻画时,引入认知主体的主观因素,利用认知逻辑中的认知模态算子,给出隐喻理解的一种认知类比逻辑模型,并将该模型应用于汉语隐喻的理解。
论文最后实现了一个隐喻机器理解系统MetaphorSuite。系统整合了句子依存结构分析及可视化修正标注功能、隐喻依存模式匹配算法及隐喻识别标注功能、隐喻映射释义功能等。MetaphorSuite系统为隐喻计算的进一步发展提供了一个完整的平台,特别是隐喻语料库和隐喻知识库等基本资源的构建。
Language understanding is one of the most important characteristics for human beings. As a pervasive phenomenon in natural language, metaphor is not only an essential thinking approach, but also an ingredient in human conceptual system. Many of our ways of thinking and experiences are virtually represented metaphorically. With the development of the cognitive research on metaphor, it is urgent to formulate a computational model for metaphor understanding based on the cognitive mechanism of metaphor, especially for promoting natural language understanding (NLU).
This dissertation first reviews the computational models for metaphor, and then addresses three key issues in metaphor computation to be resolved, which are formalization, recognition and classification of metaphor language, knowledge representation, metaphor mapping and the agent's epistemic state involved in metaphor understanding.
According to its linguistic definition, this dissertation classifies metaphorical language into two categories, i.e., denotational metaphor and connotational metaphor. Based on the dependency grammar, we describe the sentence as a dependency tree. Various dependency models of metaphorical sentences are brought forward following the analysis and classification of the dependency trees of metaphorical sentences in a large corpus. An efficient tree model matching algorithm is also proposed, which can determine the occurrence of the metaphor in the dependency tree. For denotational metaphors, a measure formula called Denotational Adaptability (DA) is given based on the hypernymy relation: if the DA value between two words is lower than the threshold, we consider the existence of a denotational metaphor. On the other hand, for collocational metaphors, the measure formula called Collocational Adaptability (CA) is given based on case and semantic knowledge base: if the CA value between two words is lower than the threshold, we consider the existence of a collocational metaphor. After the metaphor is recognized, the metaphor categories and metaphor role are tagged according to the metaphor dependency model.
As for metaphor understanding, many works have been done in pragmatics and cognitive linguistics, especially the discussion on metaphor understanding process in pragmatics and metaphor mapping representation in cognitive linguistics. In line with the conceptual blending framework, we represent conceptual knowledge as ontology, and denote mental spaces as ontologies. Therefore, metaphor mapping is converted to conceptual mapping between mental spaces. The generic space is regarded as a common ontology extended from the Sememe system of HowNet. Meanwhile, the input spaces are represented as online knowledge structures which are also represented as ontology. The input space ontology is acquired through the metaphor roles in the dependency tree, in which the source role (Met_S) and target role (Met_T) refer to concepts based on the lexical ontology and the thematic roles derived from semantic role parsing of the sentence. The blending runs the mapping algorithm on the input space ontologies, using the triangulation rule and squaring rule, and employs the Integrated Degree to measure the mapping results.
Metaphor understanding involves the agent's subjective factors, including his world knowledge of the metaphorical topic, belief and intention, etc. However, these factors are seldom touched upon by most existing research which merely focuses on the linguistic characteristics of metaphor and the representation of the world knowledge, over-emphasizing the role of objective knowledge. Therefore, in this dissertation, the agent's subjective factors are introduced. An epistemic modal operator of epistemic logic is also used in the logical characterization of metaphor understanding. The obtained logic system is applied to the comprehension of Chinese metaphors.
The final part of the dissertation implements a metaphor understanding system -MetaphorSuite. This system integrates dependency parsing and its visual revising tool, algorithm of metaphor dependency model matching and metaphor role tagging, metaphor mapping and interpretation, etc. MetaphorSuite system provides a complete platform for further development of metaphor computation, especially for the construction of basic resources, such as metaphor corpus and metaphor knowledge base.
本文从当前隐喻理论的研究热点入手,在总结现有隐喻计算模型的基础上,提出隐喻计算化研究需要解决的关键问题,就隐喻语言的形式化描述与分类识别、隐喻理解中的知识表示问题、隐喻的映射问题以及认知主体的主观因素等方面进行重点探讨。
在隐喻语言的形式化描述与分类识别问题上,根据语言学领域对隐喻语言的定义,将隐喻语言划分为指称型隐喻和搭配型隐喻两大类。以依存语法理论为句子形式化的基础,将句子描述为依存结构树,并通过对大量隐喻句依存结构树的详细分析和归类,得出隐喻句的各种依存模式,最终提出一种高效的树模式匹配算法,快速有效地识别句子依存结构树中可能存在的隐喻单元位置。针对指称型隐喻的判别,给出一种基于上下位语义关系的指称可适度量化公式:当两个词语的指称可适度低于设定的阈值时判定为存在指称隐喻;针对搭配型隐喻,提出一种基于实例和语义知识库相结合的搭配可适度量化方法:当两个词语的搭配可适度低于设定的阈值时判定存在搭配隐喻。最后根据隐喻依存模式提供的规则判断具体的隐喻类别和隐喻角色标注。
在隐喻的理解问题上,结合语用学和认知语言学在隐喻理解方面的各类观点,特别是语用学对隐喻理解过程的论述和认知语言学关于隐喻表征映射方面的研究成果,采用本体技术作为概念知识的表示方法,依据概念合成理论框架,将隐喻映射问题转化为由本体表示的心理空间之间的概念映射问题。论文把概念合成框架中的类属空间表示为由知网义原体系扩展构成的通用本体,把输入空间看作是一个在线的知识结构,同样由本体模型来描述。输入空间本体通过隐喻依存结构树中各隐喻角色来获取,其中源域角色(Met_S)和目标域角色(Met_T)经过词汇概念本体获取对应的概念,并从句子的语义角色分析和语义知识库获取相关的论元角色类型。根据三角规则和修整规则对两个输入空间本体中的概念进行映射匹配,并根据映射的整合度对各个匹配进行评估,进而形成合成空间来解释隐喻。
隐喻的理解涉及认知主体的主观认知状态,包括隐喻话题的世界知识、认知主体的信念、意图等。以往的隐喻计算研究忽略了隐喻理解过程中的这一特性,强调客观知识的作用,因而更多关注于隐喻的语言特征和世界知识的表示。基于此,本文在对隐喻理解过程进行逻辑刻画时,引入认知主体的主观因素,利用认知逻辑中的认知模态算子,给出隐喻理解的一种认知类比逻辑模型,并将该模型应用于汉语隐喻的理解。
论文最后实现了一个隐喻机器理解系统MetaphorSuite。系统整合了句子依存结构分析及可视化修正标注功能、隐喻依存模式匹配算法及隐喻识别标注功能、隐喻映射释义功能等。MetaphorSuite系统为隐喻计算的进一步发展提供了一个完整的平台,特别是隐喻语料库和隐喻知识库等基本资源的构建。
Language understanding is one of the most important characteristics for human beings. As a pervasive phenomenon in natural language, metaphor is not only an essential thinking approach, but also an ingredient in human conceptual system. Many of our ways of thinking and experiences are virtually represented metaphorically. With the development of the cognitive research on metaphor, it is urgent to formulate a computational model for metaphor understanding based on the cognitive mechanism of metaphor, especially for promoting natural language understanding (NLU).
This dissertation first reviews the computational models for metaphor, and then addresses three key issues in metaphor computation to be resolved, which are formalization, recognition and classification of metaphor language, knowledge representation, metaphor mapping and the agent's epistemic state involved in metaphor understanding.
According to its linguistic definition, this dissertation classifies metaphorical language into two categories, i.e., denotational metaphor and connotational metaphor. Based on the dependency grammar, we describe the sentence as a dependency tree. Various dependency models of metaphorical sentences are brought forward following the analysis and classification of the dependency trees of metaphorical sentences in a large corpus. An efficient tree model matching algorithm is also proposed, which can determine the occurrence of the metaphor in the dependency tree. For denotational metaphors, a measure formula called Denotational Adaptability (DA) is given based on the hypernymy relation: if the DA value between two words is lower than the threshold, we consider the existence of a denotational metaphor. On the other hand, for collocational metaphors, the measure formula called Collocational Adaptability (CA) is given based on case and semantic knowledge base: if the CA value between two words is lower than the threshold, we consider the existence of a collocational metaphor. After the metaphor is recognized, the metaphor categories and metaphor role are tagged according to the metaphor dependency model.
As for metaphor understanding, many works have been done in pragmatics and cognitive linguistics, especially the discussion on metaphor understanding process in pragmatics and metaphor mapping representation in cognitive linguistics. In line with the conceptual blending framework, we represent conceptual knowledge as ontology, and denote mental spaces as ontologies. Therefore, metaphor mapping is converted to conceptual mapping between mental spaces. The generic space is regarded as a common ontology extended from the Sememe system of HowNet. Meanwhile, the input spaces are represented as online knowledge structures which are also represented as ontology. The input space ontology is acquired through the metaphor roles in the dependency tree, in which the source role (Met_S) and target role (Met_T) refer to concepts based on the lexical ontology and the thematic roles derived from semantic role parsing of the sentence. The blending runs the mapping algorithm on the input space ontologies, using the triangulation rule and squaring rule, and employs the Integrated Degree to measure the mapping results.
Metaphor understanding involves the agent's subjective factors, including his world knowledge of the metaphorical topic, belief and intention, etc. However, these factors are seldom touched upon by most existing research which merely focuses on the linguistic characteristics of metaphor and the representation of the world knowledge, over-emphasizing the role of objective knowledge. Therefore, in this dissertation, the agent's subjective factors are introduced. An epistemic modal operator of epistemic logic is also used in the logical characterization of metaphor understanding. The obtained logic system is applied to the comprehension of Chinese metaphors.
The final part of the dissertation implements a metaphor understanding system -MetaphorSuite. This system integrates dependency parsing and its visual revising tool, algorithm of metaphor dependency model matching and metaphor role tagging, metaphor mapping and interpretation, etc. MetaphorSuite system provides a complete platform for further development of metaphor computation, especially for the construction of basic resources, such as metaphor corpus and metaphor knowledge base.
引文
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