名词词义描写和研究需要什么样的语义学知识？

该文主要讨论名词的词义描写和研究问题。首先通过对几种主要的词汇语义学理论(包括结构主义语义学、生成主义语义学、概念语义学和自然语义元语言理论)进行介绍和评述,指出它们在对名词进行语义刻画方面存在缺陷和不足;然后,重点引入生成词库理论的物性结构的描写方式,阐明它与前几种理论的区别及其自身的特点;最后,在生成词库理论的基础上,展示物性结构知识在有关名词分析中的四个研究案例(词语缺省、隐喻义生成、供用句、中动句)和在自然语言处理中的可能应用。     

On the semantics of parsing actions

Parsers, whether constructed by hand or automatically via a parser generator tool, typically need to compute some useful semantic information in addition to the purely syntactic analysis of their input. Semantic actions may be added to parsing code by hand, or the parser generator may have its own syntax for annotating grammar rules with semantic actions. In this paper, we take a functional programming view of such actions. We use concepts from the semantics of mostly functional programming languages and adapt them to give meaning to the actions of the parser. Specifically, the semantics is inspired by the categorical semantics of lambda calculi and the use of premonoidal categories for the semantics of effects in programming languages. This framework is then applied to our leading example, the transformation of grammars to eliminate left recursion. The syntactic transformation of left-recursion elimination leads to a corresponding semantic transformation of the actions for the grammar. We prove the semantic transformation correct and relate it to continuation passing style, a widely studied transformation in lambda calculi and functional programming. As an idealization of the input language of parser generators, we define a call-by-value calculus with first-order functions and a type-and-effect system where the effects are given by sequences of grammar symbols. The account of left-recursion elimination is then extended to this calculus.     

BPMN 2.0编排的形式语义和分析

BPMN 2.0编排已成为描述业务流程间交互事实上的标准.BPMN 2.0编排面向流的特征,使之会产生控制流方面的语义错误.因此,检查编排语义正确性是BPMN 2.0编排建模工具所期望具有的功能.但是,BPMN 2.0标准规约中编排缺少形式语义及相应的分析技术,这阻碍了对BPMN 2.0编排的语义分析.本文提出了一种映射,用于将BPMN 2.0编排转换为工作流网,使用Petri网来形式定义BPMN 2.0编排的语义.借助Petri网的分析技术,这种定义的语义可用来分析BPMN 2.0编排的结构和控制流方面的错误.该映射和语义分析已被实现为一种工具.实验结果表明,这种形式化可以识别BPM AI过程模型库中编排的语义错误.     

藏语语料库词类描述方法研究

藏语词类的基本属性描述是藏语语料库多级加工的基础,根据藏语语料库多级加工的实际需要,通过对藏语词类的语法、语义信息的描述和藏语词语后添加方式的分析,可以为计算机对藏文的词语搭配结构、语法信息、语义信息和词类标记进行自动处理提供一个切实可行的方法。     

Semantics of OCL specified with QVT

The Object Constraint Language (OCL) has been for many years formalized both in its syntax and semantics in the language standard. While the official definition of OCL’s syntax is already widely accepted and strictly supported by most OCL tools, there is no such agreement on OCL’s semantics, yet. In this paper, we propose an approach based on metamodeling and model transformations for formalizing the semantics of OCL. Similarly to OCL’s official semantics, our semantics formalizes the semantic domain of OCL, i.e. the possible values to which OCL expressions can evaluate, by a metamodel. Contrary to OCL’s official semantics, the evaluation of OCL expressions is formalized in our approach by model transformations written in QVT. Thanks to the chosen format, our semantics definition for OCL can be automatically transformed into a tool, which evaluates OCL expressions in a given context. Our work on the formalization of OCL’s semantics resulted also in the identification and better understanding of important semantic concepts, on which OCL relies. These insights are of great help when OCL has to be tailored as a constraint language of a given DSL. We show on an example, how the semantics of OCL has to be redefined in order to become a constraint language in a database domain.

基于桥连接的词典学习方法的语义解析

现阶段的语义解析方法大部分都基于组合语义,这类方法的核心就是词典。词典是词汇的集合,词汇定义了自然语言句子中词语到知识库本体中谓词的映射。语义解析一直面临着词典中词汇覆盖度不够的问题。针对此问题,该文在现有工作的基础上,提出了基于桥连接的词典学习方法,该方法能够在训练中自动引入新的词汇并加以学习,为了进一步提高新学习到的词汇的准确度,该文设计了新的词语—二元谓词的特征模板,并使用基于投票机制的核心词典获取方法。该文在两个公开数据集(WebQuestions和Free917)上进行了对比实验,实验结果表明,该文方法能够学习到新的词汇,提高词汇的覆盖度,进而提升语义解析系统的性能,特别是召回率。     

一个基于语义的中文事件论元抽取方法

中文是一种话题结构的语言,其表达方式比较灵活,但句法结构不如英文严谨,导致了事件中论元与触发词的关系较松散。现有的论元抽取方法多数是基于浅层语义的句法结构特征,从而造成了论元抽取性能低下。为了解决这个问题,提出了基于语义的中文事件论元抽取方法。该方法利用角色、实体和触发词的语义,弥补了论元抽取中单纯采用句法特征的缺陷。在ACE2005中文语料上的测试结果表明,该方法与基准系统相比具有更高的性能。     

A semantic service discovery approach for ubiquitous computing

Services in the ubiquitous computing are heterogeneous in nature. To be pervasive, these services should be defined in terms of their functionality and capabilities rather than the meaningless Universally Unique IDentifiers (UUIDs) or types of services. With that, clients can access the proper service based on semantic requests, rather then a pre-configured profile. In this paper, we study the requirements for semantic query to be feasible in service discovery processes. Current discovery protocols and the concept of semantics are brought together to construct a framework to realize the semantic service discovery for ubiquitous computing. Many issues are discussed in relation to service discovery topologies, ontology languages, and semantic query languages.     

Interactive semantics

Much research pursues machine intelligence through better representation of semantics. What is semantics? People in different areas view semantics from different facets although it accompanies interaction through civilization. Some researchers believe that humans have some innate structure in mind for processing semantics. Then, what the structure is like? Some argue that humans evolve a structure for processing semantics through constant learning. Then, how the process is like? Humans have invented various symbol systems to represent semantics. Can semantics be accurately represented? Turing machines are good at processing symbols according to algorithms designed by humans, but they are limited in ability to process semantics and to do active interaction. Super computers and high-speed networks do not help solve this issue as they do not have any semantic worldview and cannot reflect themselves. Can future cyber-society have some semantic images that enable machines and individuals (humans and agents) to reflect themselves and interact with each other with knowing social situation through time? This paper concerns these issues in the context of studying an interactive semantics for the future cyber-society. It firstly distinguishes social semantics from natural semantics, and then explores the interactive semantics in the category of social semantics. Interactive semantics consists of an interactive system and its semantic image, which co-evolve and influence each other. The semantic worldview and interactive semantic base are proposed as the semantic basis of interaction. The process of building and explaining semantic image can be based on an evolving structure incorporating adaptive multi-dimensional classification space and self-organized semantic link network. A semantic lens is proposed to enhance the potential of the structure and help individuals build and retrieve semantic images from different facets, abstraction levels and scales through time.     

Enhanced Topic-based Vector Space Model for semantics-aware spam filtering

Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms and phishing. More than 85% of received e-mails are spam. Historical approaches to combat these messages including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Currently, many solutions feature machine-learning algorithms trained using statistical representations of the terms that usually appear in the e-mails. Still, these methods are merely syntactic and are unable to account for the underlying semantics of terms within the messages. In this paper, we explore the use of semantics in spam filtering by representing e-mails with a recently introduced Information Retrieval model: the enhanced Topic-based Vector Space Model (eTVSM). This model is capable of representing linguistic phenomena using a semantic ontology. Based upon this representation, we apply several well-known machine-learning models and show that the proposed method can detect the internal semantics of spam messages.     

汉语动词资源馆(Chinese Verb Library)的构建

该研究以动词的语义聚合层次为核心,构建了含有四个语义层级的动词分类系统,依托生成词库理论、语义格框理论和构式语法理论,从事件结构、语义格框架、物性角色、句法格式四个角度来对动词的自身属性以及组合性等特征进行描述,从而构造出能够解释并深入描述动动之间、动名之间甚至是超常搭配用法的汉语动词资源馆Chinese Verb Library(CVL)。实验表明,该研究可以为句法分析、语义角色标注、尤其是揭示隐含的谓间关系提供新的支持。     

基于语义的VSM模型改进

文本聚类在很多领域都有广泛的应用,传统的文本聚类方法由于并不考虑语义因素,得出的聚类效果并不理想.利用语义对VSM模型进行变换,即基于语义对VSM模型的各维进行扭曲,将原本的正交坐标系基于语义变换为斜角坐标系,然后将文本的特征向量映射到变换后的VSM模型上再进行聚类,相对减小语义相关的特征向量间的语义距离,从而提高了文...     

Preferential Accessibility and Preferred Worlds

Modal accounts of normality in non-monotonic reasoning traditionally have an underlying semantics based on a notion of preference amongst worlds. In this paper, we motivate and investigate an alternative semantics, based on ordered accessibility relations in Kripke frames. The underlying intuition is that some world tuples may be seen as more normal, while others may be seen as more exceptional. We show that this delivers an elegant and intuitive semantic construction, which gives a new perspective on defeasible necessity. Technically, the revisited logic does not change the expressive power of our previously defined preferential modalities. This conclusion follows from an analysis of both semantic constructions via a generalisation of bisimulations to the preferential case. Reasoners based on the previous semantics therefore also suffice for reasoning over the new semantics. We complete the picture by investigating different notions of defeasible conditionals in modal logic that can also be captured within our framework.     

基于轨迹的程序语义之一：轨迹与语义对象

本文提出一种基于轨迹的指称语义框架,该框架结合了操作语义和代数语义的特征,避免使用专门的数学理论,将静态语义和动态语义结合在一起统一处理.本文及其续篇将通过一个中等规模的过程式模型语言来说明上述语义框架更适合描述真正的程序设计语言.本文首先引入轨迹概念和模型语言,然后讨论该语言的各句法成分所对应的语义论域,其中没有使用含有函数空间构造运算的递归论域方程.     

基于层次体系的情感单元表示方法

情感词是情感分析中的基础单元,因此情感词典在情感分析中起着决定性的作用,目前构建情感词典的方法只是用到了单词的语义信息和构词信息,忽略了其所在语境。基于此,对于一些语义未知的词,传统语义方法难以得出其情感权重,而对于一些由于语境变化而产生新用法的词,使用语义方法很难计算出其真实权重。针对这种情况,首先提出了从构字到篇章的情感分析层次体系,每层都有对应到上层的表示方法和情感值计算公式,将分析单元细分到单词维度。在此基础上,提出了基于词语构字和语境的情感语义单元自动构建方法。该方法利用已知情感词典,同时根据情感词的构字和情感词的语境情感倾向计算该词的情感权重,得到的结果更加准确。在社交网络真实数据集上的实验表明,本文方法构建的情感单元较之前的方法在准确率上有3%的提升。同时,情感单元可直接用到情感分析任务中,情感分析的准确率在基于规则的情感分析实验中有9%的提升,在深度学习方法上有3%的提升。     

The rewriting logic semantics project

Rewriting logic is a flexible and expressive logical framework that unifies algebraic denotational semantics and structural operational semantics (SOS) in a novel way, avoiding their respective limitations and allowing succinct semantic definitions. The fact that a rewrite logic theory’s axioms include both equations and rewrite rules provides a useful “abstraction dial” to find the right balance between abstraction and computational observability in semantic definitions. Such semantic definitions are directly executable as interpreters in a rewriting logic language such as Maude, whose generic formal tools can be used to endow those interpreters with powerful program analysis capabilities.