隐喻计算模型及其在隐喻分类上的应用

隐喻理解是语篇理解中的难点，也是认知科学研究中的热点。本文针对汉语中普遍存在隐喻的现象，从计算的角度，首先提出了一套汉语句子形式化方法，并在此基础上针对隐喻的特点构建出隐喻语义网络计算模型。为了阐述隐喻网络模型的应用，文章后半部分提出了一套基于隐喻网络模型的隐喻分类体系，并对其合理性以及实用性进行了分析，为后续的隐喻自动识别以及隐喻理解奠定了基础。     

隐喻理解的计算模型综述

国际上，隐喻在思维及语言中所处的中心地位正逐渐引起人工智能研究者的重视。但在国内学术界，还鲜有开展隐喻计算化这方面研究的；实际上，作为异常用法的隐喻现象是自然语言中的普遍情况，因此隐喻问题若得不到很好的解决，将成为制约自然语言理解和机器翻译的瓶颈问题。本文结合相关的隐喻理论基础，根据不同的计算路线对已有隐喻理解计算模型进行分类，包括基于语义优先方法、基于知识表示的方法、基于逻辑的方法和基于统计语料库的方法，并在分析这些方法的适用范围和优缺点的基础上，对隐喻的计算理解方法以及面向汉语的隐喻理解计算模型研究提出了展望和建议。     

中文阅读理解语料库构建技术研究

阅读理解问答系统指的是能够自动分析一个自然语言文章,并且根据文中的信息为每个问题生成一个答案的系统,具有很高的研究价值。然而,缺乏中文阅读理解语料库已经成为制约汉语阅读理解问答系统发展的主要障碍。本文对于中文阅读理解语料库的构建过程进行了详细的介绍,包括语料选材、编写问句,标注答案句、语料加工和评测机制,尤其是基于汉语框架语义知识库对语料进行了框架元素、短语类型和句法功能三个层面标注的深加工技术。     

基于实例的汉语语义超常搭配的自动发现

搭配在语言信息处理中具有重要的应用价值,通常我们主要关注符合语法规则的常规搭配.实际上,语言中还存在着大量的语法上符合规则而语义上不符合常规认知的语义超常搭配现象,而这样的现象与语言的隐喻表达和思维有着密切的联系,对自然语言理解将产生重要的影响.本文面向汉语隐喻理解来研究文本中语义超常搭配的自动发现方法,从汉语语义超常搭配判断的心理机制出发,提出了基于实例的汉语语义超常搭配识别的量化计算方法.实验以动词为中心的搭配语料为测试集,语义超常搭配识别的召回率为80.7%,准确率为81.5%.实验结果表明本文所给出的基于实例语义超常搭配判断的办法是切实可行的.     

隐喻自动处理研究进展

隐喻在人类语言中普遍存在,是自然语言理解必须面对的问题。该文首先探讨了对隐喻的认识及语言中隐喻表达的分类。把隐喻自动处理分为隐喻识别、隐喻理解和隐喻生成三个子任务,对以往的研究成果进行梳理,着重介绍近几年来隐喻自动处理研究的新成果、新特点。隐喻自动处理离不开隐喻知识库的支持,文章也介绍了国内外隐喻知识库建设的主要成果。隐喻自动处理的目的是为了提高自然语言处理的智能化水平,文章探讨了隐喻处理在自然语言处理任务中的应用。最后展望了汉语隐喻自动处理研究的前景。     

一种基于聚类与分类结合的汉语隐喻短语识别方法

隐喻识别是自然语言处理的一个重要研究分支。目前人们越来越清楚地认识到隐喻在思维及语言中所处的重要地位。本研究在前人工作的实验和考察基础上,发现基于分类器来识别隐喻的方法存在数据稀疏的问题,即当训练语料中缺少需要识别的源域词数据时,分类的结果将不会太好。应对数据稀疏问题,该文提出了一种基于聚类与分类结合的隐喻短语获取方法。该方法将包含源域词S的短语进行聚类。将聚类的结果作为分类的一类特征。实验表明,使用聚类产生的特征训练出来的分类器,不仅能很好地识别训练语料中存在源域词数据的情况,也能很好地识别训练语料中缺少源域词数据的情况,具有很高的召回率。     

汉语组块分析在情感分类中的应用研究

网络产品评论的情感分析对网络用户的日常购买行为有着重要的决策作用,因此,如何利用细粒化的处理方法提高情感分析的准确率,成为了一个热门的研究话题。针对该问题提出一种基于汉语组块分析的情感识别方法,首先依靠汉语组块分析对汽车评论语料进行细粒化的处理并提取情感标签,再结合情感词本体和支持向量机模型对情感标签进行分类,从而实现情感倾向性的判别。实验表明,采用汉语组块分析的情感分类方法相比其他的分类算法平均准确率提高了4%。因此,基于汉语组块分析的情感分类可以降低分类器的输入特征维数,并有效提高分类器的分类性能。     

Co-training机器学习方法在中文组块识别中的应用

采用半指导机器学习方法co2training 实现中文组块识别。首先明确了中文组块的定义,co-training 算法的形式化定义。文中提出了基于一致性的co-training 选取方法将增益的隐马尔可夫模型(Transductive HMM) 和基于转换规则的分类器(fnTBL) 组合成一个分类体系,并与自我训练方法进行了比较,在小规模汉语树库语料和大规模未带标汉语语料上进行中文组块识别,实验结果要比单纯使用小规模的树库语料有所提高,F 值分别达到了85134 %和83141 % ,分别提高了2113 %和7121 %。     

汉语语义知识的自动获取方法研究

在信息处理领域，对语言的理解十分重要，而这离不开语义知识的使用。本文建立了基于框架的汉语语义知识表述体系，研究了基于语料的语义知识的自动获取方法。经过实验，得到大量语义规则，并用其进行义项消歧实验，取得了较好的结果。     

基于特征相关性的汉语文本自动分类模型的研究

本文提出一种基于预定义类别与文本特征之间相关性的自动分类算法，并在文中详细阐述了汉语文本自动分类模型的设计与实现过程。为测试分类模型实现性能，建立具有１２类别的分类体系，并构造包含近５００篇汉语新闻语料的测试集。实验结果表明，评价自动分类算法的两个重要指标：查全率和查准率，都比较令人满意。     

汉语隐喻理解的逻辑描述初探

隐喻在语篇中出现非常普遍,是语言认知和计算机语篇理解中重要的一环。但无论是其逻辑基础或实验系统的探索都处于初级阶段。本文从逻辑角度给出了隐喻逻辑的定义、建构和性质。同时,针对汉语文本中名词性隐喻、动词性隐喻等类型,细化了隐喻逻辑的规则,并利用细化后的规则,分析了隐喻句中隐含信息的发掘方法,为隐喻的计算提供了新的解决方案。分析的结果表明,本文所提出的隐喻逻辑对汉语隐喻意义的生成有很好的解释能力,为后续隐喻句的计算机处理提供了理论基础。     

An Ontology-Based Approach to Metaphor Cognitive Computation

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, especially with the view to promoting natural language understanding. Many works have been done in pragmatics and cognitive linguistics, especially the discussions on metaphor understanding process in pragmatics and metaphor mapping representation in cognitive linguistics. In this paper, a theoretical framework for metaphor understanding based on the embodied mechanism of concept inquiry is proposed. Based on this framework, ontology is introduced as the knowledge representation method in metaphor understanding, and metaphor mapping is formulated as ontology mapping. In line with the conceptual blending theory, a revised conceptual blending framework is presented by adding a lexical ontology and context as the fifth mental space, and a metaphor mapping algorithm is proposed.     

METAPHOR RECOGNITION: CHMETA, A PATTERN-BASED SYSTEM

Metaphor recognition presents a computational challenge, in part due to metaphoric deviation from literal thinking, and also because of a metaphor's various linguistic expressions. This article forwards a new computational method, an integrated treatment of metaphor recognition from the computational perspective, which recent related studies have not entirely addressed. The authors differentiate metaphor recognition from complex metaphor inference and interpretation employing psychological clues. To accomplish this, we have developed a formalized system of metaphorical expression in metaphor role dependency schema, which specifically defines, classifies, and quantifies metaphorical anomalies, building a computable classification system for metaphors (incorporating 32 major patterns of metaphorical expressions) by providing a strategy to locate potential metaphorical anomalies in a target input sentence through a pattern recognition method and a metaphor components' tagging approach. This metaphor recognition and tagging system is named and implemented as "CHMeta." Experiment results support the validity and efficiency of this metaphor recognition system. Compared with most metaphor computation systems, which work mainly on a few examples, this system classifies major metaphorical expressions from a computational perspective and is able to recognize a variety of different kinds of metaphors, including nested ones. Thus, this is the first integrated work in computable classification, recognition, and tagging of large-scale metaphors in Chinese.     

汉语名词的隐喻知识表示及获取研究

隐喻知识的表示和获取是进行隐喻计算的基础。该文把隐喻知识看作是本体和喻体的特征和属性之间的关联。主要通过惯用语导入和句法模式识别两种机制,来获取名词的隐喻知识。惯用语的隐喻比较固定和单一,从专门的词典释义中就能获取准确的隐喻特征和属性。而一般名词的隐喻知识比较复杂,该文主要依托语料库和搜索引擎,通过关键词和句法匹配来获取同一名词不同的隐喻特征及对应属性。该项工作的结果,对隐喻句隐喻意义的获取和名词语义属性特征的描述体系构建具有一定的价值。     

CONCEPTUAL SCAFFOLDING: A SPATIALLY FOUNDED MEANING REPRESENTATION FOR METAPHOR COMPREHENSION

Once viewed as a rhetorical and superficial language phenomenon, metaphor is now recognized to serve a fundamental role in our conceptual structuring and language comprehension processes. In particular, it is argued that certain experiential metaphors based upon intuitions of spatial relations are inherent in the conceptual organization of our most abstract thoughts. In this paper we present a two-stage computational model of metaphor interpretation which employs a spatially founded semantics to broadly characterize the meaning carried by a metaphor in terms of a conceptual scaffolding , an interim meaning structure around which a fuller interpretation is fleshed out over time. We then present a semantics for the construction of conceptual scaffolding which is based upon core metaphors of collocation, containment and orientation. The goal of this scaffolding is to maintain the intended association of ideas even in contexts in which system knowledge is insufficient for a complete interpretation. This two-stage system of scaffolding and elaboration also models the common time lapse between initial metaphor comprehension and full metaphor appreciation. Several mechanisms for deriving elaborative inference from scaffolding structures, particularly in cases of novel or creative metaphor, are also presented. While the system developed in this paper has significant practical application, it also demonstrates that core spatial metaphors clearly play a central role in metaphor comprehension.     

Computational Mechanisms for Metaphor in Languages: A Survey

Metaphor computation has attracted more and more attention because metaphor, to some extent, is the focus of mind and language mechanism. However, it encounters problems not only due to the rich expressive power of natural language but also due to cognitive nature of human being. Therefore machine-understanding of metaphor is now becoming a bottle-neck in natural language processing and machine translation. This paper first suggests how a metaphor is understood and then presents a survey of current computational approaches, in terms of their linguistic historical roots, underlying foundations, methods and techniques currently used, advantages, limitations, and future trends. A comparison between metaphors in English and Chinese languages is also introduced because compared with development in English language Chinese metaphor computation is just at its starting stage. So a separate summarization of current progress made in Chinese metaphor computation is presented. As a conclusion, a few suggestions are proposed for further research on metaphor computation especially on Chinese metaphor computation.