A Note on Intensionalization

Building on Ben-Avi and Winter’s (2007) work, this paper provides a general “intensionalization” procedure that turns an extensional semantics for a language into an intensionalized one that is capable of accommodating “truly intensional” lexical items without changing the compositional semantic rules. We prove some formal properties of this procedure and clarify its relation to the procedure implicit in Montague’s (1973) PTQ.     

On open‐set lattices and some of their applications in semantics

In this article, we present the theory of Kripke semantics, along with the mathematical framework and applications of Kripke semantics. We take the Kripke‐Sato approach to define the knowledge operator in relation to Hintikka's possible worlds model, which is an application of the semantics of intuitionistic logic and modal logic. The applications are interesting from the viewpoint of agent interactives and process interaction. We propose (i) an application of possible worlds semantics, which enables the evaluation of the truth value of a conditional sentence without explicitly defining the operator “→” (implication), through clustering on the space of events (worlds) using the notion of neighborhood; and (ii) a semantical approach to treat discrete dynamic process using Kripke‐Beth semantics. Starting from the topological approach, we define the measure‐theoretical machinery, in particular, we adopt the methods developed in stochastic process—mainly the martingale—to our semantics; this involves some Boolean algebraic (BA) manipulations. The clustering on the space of events (worlds), using the notion of neighborhood, enables us to define an accessibility relation that is necessary for the evaluation of the conditional sentence. Our approach is by taking the neighborhood as an open set and looking at topological properties using metric space, in particular, the so‐called ε‐ball; then, we can perform the implication by computing Euclidean distance, whenever we introduce a certain enumerative scheme to transform the semantic objects into mathematical objects. Thus, this method provides an approach to quantify semantic notions. Combining with modal operators K_i operating on E set, it provides a more‐computable way to recognize the “indistinguishability” in some applications, e.g., electronic catalogue. Because semantics used in this context is a local matter, we also propose the application of sheaf theory for passing local information to global information. By looking at Kripke interpretation as a function with values in an open‐set lattice ??_U, which is formed by stepwise verification process, we obtain a topological space structure. Now, using the measure‐theoretical approach by taking the Borel set and Borel function in defining measurable functions, this can be extended to treat the dynamical aspect of processes; from the stochastic process, considered as a family of random variables over a measure space (the probability space triple), we draw two strong parallels between Kripke semantics and stochastic process (mainly martingales): first, the strong affinity of Kripke‐Beth path semantics and time path of the process; and second, the treatment of time as parametrization to the dynamic process using the technique of filtration, adapted process, and progressive process. The technique provides very effective manipulation of BA in the form of random variables and σ‐subalgebra under the cover of measurable functions. This enables us to adopt the computational algorithms obtained for stochastic processes to path semantics. Besides, using the technique of measurable functions, we indeed obtain an intrinsic way to introduce the notion of time sequence. © 2003 Wiley Periodicals, Inc.     

From Interface to Correspondence: Recovering Classical Representations in a Pragmatic Theory of Semantic Information

One major fault line in foundational theories of cognition is between the so-called “representational” and “non-representational” theories. Is it possible to formulate an intermediate approach for a foundational theory of cognition by defining a conception of representation that may bridge the fault line? Such an account of representation, as well as an account of correspondence semantics, is offered here. The account extends previously developed agent-based pragmatic theories of semantic information, where meaning of an information state is defined by its interface role, to a theory that accommodates a notion of representation and correspondence semantics. It is argued that the account can be used to develop an intermediate approach to cognition, by showing that the major sources of tension between “representational” and “non-representational” theories may be eased.     

Topological spatial relations for active visual search

If robots are to assume their long anticipated place by humanity’s side and be of help to us in our partially structured environments, we believe that adopting human-like cognitive patterns will be valuable. Such environments are the products of human preferences, activity and thought; they are imbued with semantic meaning. In this paper we investigate qualitative spatial relations with the aim of both perceiving those semantics, and of using semantics to perceive. More specifically, in this paper we introduce general perceptual measures for two common topological spatial relations, “on” and “in”, that allow a robot to evaluate object configurations, possible or actual, in terms of those relations. We also show how these spatial relations can be used as a way of guiding visual object search. We do this by providing a principled approach for indirect search in which the robot can make use of known or assumed spatial relations between objects, significantly increasing the efficiency of search by first looking for an intermediate object that is easier to find. We explain our design, implementation and experimental setup and provide extensive experimental results to back up our thesis.     

中文词义关系的定义与判定原则

在英语及其它的欧洲语言里,词汇语意关系已有相当充分的研究。例如,欧语词网( EuroWordNet ,Vossen 1998) 就是一个以语意关系来勾勒词汇词义的数据库。也就是说,词汇意义的掌握是透与其它词汇语意的关连来获致的。为了确保数据库建立的品质与一致性,欧语词网计画就每一个处理的语言其词汇间的词义关系是否成立提出相应的语言测试。实际经验显示,利用这些语言测试,人们可以更容易且更一致地辨识是否一对词义之间确实具有某种词义关系。而且,每一个使用数据库的人也可以据以检验其中关系连结的正确性。换句话说,对一个可检验且独立于语言的词汇语意学理论而言,这些测试提供了一个基石。本文中,我们探究为中文词义关系建立中文语言测试的可能性。尝试为一些重要的语意关系提供测试的句式和规则来评估其可行性。这项研究除了建构中文词汇语意学的理论基础,也对Miller的词汇网络架构(WordNet ,Fellbaum 1998) 提供了一个有力的支持,这个架构在词汇表征和语言本体架构研究上开拓了关系为本的进路。     

词汇计量研究与常用词知识库建设

面向自然语言处理的词汇语义研究应该以词汇的计量研究为基础。该文在评述汉语词汇计量研究的主要成果以后,提出一个汉语常用词知识库的建设任务,并给出常用词表的构造性定义、词表常用性的定量评价方法以及“部件词”的概念,最后介绍现代汉语常用词知识库的总体设计和已经做的工作。期望常用词知识库的建设能为汉语词汇语义学研究、为中文信息处理事业的发展做出贡献。     

支持虚拟组织的语义基础设施的动态构建方法研究

提出一种从虚拟组织自治域的资源描述中抽取语义,然后聚合为虚拟组织的语义基础设施的方法．该方法引入了一种领域知识学习算法,用以建立当前语境相关的词法空间,以提高语义抽取和聚合的准确性及自动化程度,并且在语义聚合的过程中隐含了虚拟组织语义到自治域语义的映射,更好的支持了虚拟组织应用的构建和跨自治域资源的透明访问．实验表明,该方法能够适应虚拟组织的动态开放环境、有效支持虚拟组织的语义基础设施构建．     

《同义词词林》在中文实体关系抽取中的作用

语义信息在命名实体间语义关系抽取中具有重要的作用。该文以《同义词词林》为例,系统全面地研究了词汇语义信息对基于树核函数的中文语义关系抽取的有效性,深入探讨了不同级别的语义信息和一词多义等现象对关系抽取的影响,详细分析了词汇语义信息和实体类型信息之间的冗余性。在ACE2005中文语料库上的关系抽取实验表明,在未知实体类型的前提下,语义信息能显著提高抽取性能;而在已知实体类型的情况下,语义信息也能明显提高某些关系类型的抽取性能,这说明《词林》语义信息和实体类型信息在中文语义关系抽取中具有一定的互补性。     

Semantics enactment for interoperability assessment in enterprise information systems

The grown complexity of the modern enterprise poses a series of challenges, among them keeping competitiveness in the fast changing environment in which the enterprise evolves. Addressing enterprise integration is considered as a key to achieve the goal of any enterprise either it is a single or a networked enterprise. Enterprise modelling is a prerequisite to enable the common understanding of the enterprises and its various interactions in order to “provide the right information, at the right time, at the right place”. However, problems often emerge from a lack of understanding of the semantics of the elaborated models resulting from various modelling experience based on different methods and tools. This paper describes the challenges associated to semantics enactment in information systems models. To facilitate this enactment, it proposes an approach based on a fact-oriented modelling perspective. Then, it also provides an algorithm to automatically build semantic aggregates that help in highlighting enterprise models core embedded semantics. A case study on the field of B2M interoperability is performed in order to illustrate the application of the presented approach.     

A new semantic relatedness measurement using WordNet features

Computing semantic similarity/relatedness between concepts and words is an important issue of many research fields. Information theoretic approaches exploit the notion of Information Content (IC) that provides for a concept a better understanding of its semantics. In this paper, we present a complete IC metrics survey with a critical study. Then, we propose a new intrinsic IC computing method using taxonomical features extracted from an ontology for a particular concept. This approach quantifies the subgraph formed by the concept subsumers using the depth and the descendents count as taxonomical parameters. In a second part, we integrate this IC metric in a new parameterized multistrategy approach for measuring word semantic relatedness. This measure exploits the WordNet features such as the noun “is a” taxonomy, the nominalization relation allowing the use of verb “is a” taxonomy and the shared words (overlaps) in glosses. Our work has been evaluated and compared with related works using a wide set of benchmarks conceived for word semantic similarity/relatedness tasks. Obtained results show that our IC method and the new relatedness measure correlated better with human judgments than related works.     

Advanced computational reasoning based on the NKRL conceptual model

After having recalled some well-known shortcomings linked with the Semantic Web approach to the creation of (application oriented) systems of “rules” – e.g., limited expressiveness, adoption of an Open World Assumption (OWA) paradigm, absence of variables in the original definition of OWL – this paper examines the technical solutions successfully used for implementing advanced reasoning systems according to the NKRL’s methodology. NKRL (Narrative Knowledge Representation Language) is a conceptual meta-model and a Computer Science environment expressly created to deal, in an ‘intelligent’ and complete way, with complex and content-rich non-fictional ‘narrative’ data sources. These last include corporate memory documents, news stories, normative and legal texts, medical records, surveillance videos, actuality photos for newspapers and magazines, etc. In this context, we will expound first the need for distinguishing between “plain/static” and “structured/dynamic” knowledge and for introducing appropriate (and different) knowledge representation structures for these two types of knowledge. In a structured/dynamic context, we will then show how the introduction of “functional roles” – associated with the possibility of making use of n-ary structures – allows us to build up highly ‘expressive’ rules whose “atoms” can directly represent complex situations, actions, etc. without being restricted to the use of binary clauses. In an NKRL context, “functional roles” are primitive symbols interpreted as “relations” – like “subject”, “object”, “source”, “beneficiary”, etc. – that link a semantic predicate with its arguments within an n-ary conceptual formula. Functional roles contrast then with the “semantic roles” that are equated to ordinary concepts like “student”, to be inserted into the “non-sortal” (no direct instances) branch of a traditional ontology.     

BFS-CTC汉语句义结构标注语料库

句义结构分析是汉语语义分析中不可逾越的重要环节,为了满足汉语句义结构分析的需要,基于现代汉语语义学理论构建了一种层次化的汉语句义结构模型,定义了标注规范和标记形式,建设了一个汉语句义结构标注语料库BFS-CTC(Beijing Forest Studio-Chinese Tagged Corpus)。标注内容方面,基于句义结构模型的定义标注了句义结构句型层、描述层、对象层和细节层中所包含的各个要素及其组合关系,包括句义类型、谓词及其时态、语义格类型等信息,并且提供了词法和短语结构句法信息,便于词法、句法、句义的对照分析研究;语料库组织结构方面,该语料库包括四个部分,即原始句子库、词法标注库、句法标注库和句义结构标注库,可根据研究的需要,在词法、句法、句义结构标注的基础上进行深加工,在核心标注库的基础上添加更多具有针对性的扩展标注库,利用句子的唯一ID号进行识别和使用;语料来源和规模方面,语料全部来自新闻语料,经过人工收集、整理,合理覆盖了主谓句、非主谓句、把字句等六种主要句式类型,规模已达到10 000句。同其他语义标注库相比,BFS-CTC基于现代汉语语义学,提供了多层次的句义结构标注信息,兼容进行了词法和语法标注,各类标注既可以单独使用也可综合使用进行横向分析,可用于自然语言处理多方面的研究,进一步推动汉语语义分析的研究和发展。     

SYNTACTIC SIMPLIFICATION AND SEMANTIC ENRICHMENT—TRIMMING DEPENDENCY GRAPHS FOR EVENT EXTRACTION

In our approach to event extraction, dependency graphs constitute the fundamental data structure for knowledge capture. Two types of trimming operations pave the way to more effective relation extraction. First, we simplify the syntactic representation structures resulting from parsing by pruning informationally irrelevant lexical material from dependency graphs. Second, we enrich informationally relevant lexical material in the simplified dependency graphs with additional semantic meta data at several layers of conceptual granularity. These two aggregation operations on linguistic representation structures are intended to avoid overfitting of machine learning‐based classifiers which we use for event extraction (besides manually curated dictionaries). Given this methodological framework, the corresponding JReX system developed by the Julie Lab Team from Friedrich‐Schiller‐Universität Jena (Germany) scored on 2nd rank among 24 competing teams for Task 1 in the “BioNLP’09 Shared Task on Event Extraction,” with 45.8% recall, 47.5% precision and 46.7% F1‐score on all 3,182 events. In more recent experiments, based on slight modifications of JReX and using the same data sets, we were able to achieve 45.9% recall, 57.7% precision, and 51.1% F1‐score.     

Semantic Similarity Assessment Using Differential Evolution Algorithm in Continuous Vector Space

The assessment of semantic similarity between terms is one of the challenging tasks in knowledge-based applications, such as multimedia retrieval, automatic service discovery and emotion mining. By means of similarity estimation, the comprehension of textual resources can become more feasible and accurate. Some studies have proposed the integration of various assessment methods for taking advantage of different semantic resources, but most of them simply employ average operation or regression training. In this paper, we address this problem by combining the corpus-based similarity methods with the WordNet-based methods based on a differential evolution (DE) algorithm. Specifically, this DE-based approach conducts similarity assessment in a continuous vector space. It is validated against a variety of similarity approaches on multiple benchmark datasets. Empirical results demonstrate that our approach outperforms existing works and more conforms to the human judgement of similarity. The results also prove the expressiveness of continuous vectors learned from neural network on latent lexical semantics.     

句法网与语义网的对比研究

基于网络观的语言研究已经成为语言分析的趋势之一。但不同语言单位层级、不同语言单位关系的选取导致了语言网络的差异。从词的同现网到句法网再到语义网所需要的语言学知识也逐步深化,该文旨在构建语义学理论支撑的语义网络,并把虚词纳入语义分析过程,分别以句法关系和语义关系作为联结,用Cytoscape构建了句法网和语义网。结果发现: 语义网的直径、平均最短距离比句法网大,层级性比句法网差,聚集系数比句法网小,虚词节点“的”“和”“个”等有可能是局部的中心节点。     

A WEB‐BASED PERSONALIZED BUSINESS PARTNER RECOMMENDATION SYSTEM USING FUZZY SEMANTIC TECHNIQUES

The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking “one‐to‐one” e‐services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e‐services. Recommender systems is an effective approach for the implementation of Personalized E‐Service which has gained wide exposure in e‐commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item‐based fuzzy semantic similarity and item‐based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF‐based recommendation approaches, namely sparsity and new “cold start” item problems.     

Evocation: analyzing and propagating a semantic link based on free word association

Studies of lexical–semantic relations aim to understand the mechanism of semantic memory and the organization of the mental lexicon. However, standard paradigmatic relations such as “hypernym” and “hyponym” cannot capture connections among concepts from different parts of speech. WordNet, which organizes synsets (i.e., synonym sets) using these lexical–semantic relations, is rather sparse in its connectivity. According to WordNet statistics, the average number of outgoing/incoming arcs for the hypernym/hyponym relation per synset is 1.33. Evocation, defined as how much a concept (expressed by one or more words) brings to mind another, is proposed as a new directed and weighted measure for the semantic relatedness among concepts. Commonly applied semantic relations and relatedness measures do not seem to be fully compatible with data that reflect evocations among concepts. They are compatible but evocation captures MORE. This work aims to provide a reliable and extendable dataset of concepts evoked by, and evoking, other concepts to enrich WordNet, the existing semantic network. We propose the use of disambiguated free word association data (first responses to verbal stimuli) to infer and collect evocation ratings. WordNet aims to represent the organization of mental lexicon, and free word association which has been used by psycholinguists to explore semantic organization can contribute to the understanding. This work was carried out in two phases. In the first phase, it was confirmed that existing free word association norms can be converted into evocation data computationally. In the second phase, a two-stage association-annotation procedure of collecting evocation data from human judgment was compared to the state-of-the-art method, showing that introducing free association can greatly improve the quality of the evocation data generated. Evocation can be incorporated into WordNet as directed links with scales, and benefits various natural language processing applications.     

EMOTIONAL AND NON-EMOTIONAL PERSUASION

A relevant issue in the domain of natural argumentation and persuasion is the interaction (synergic or conflicting) between “rational” or “cognitive” modes of persuasion and “irrational” or “emotional” ones. This work provides a model of general persuasion and emotional persuasion. We examine two basic modes for appealing to emotions, arguing that emotional persuasion does not necessarily coincide with irrational persuasion, and showing how the appeal to emotions is grounded on the strict and manifold relationship between emotions and goals, which is, so to say, “exploited” by a persuader. We describe various persuasion strategies, propose a method to formalize and represent them as oriented graphs, and show how emotional and non-emotional strategies (and also emotional and non-emotional components in the same strategy) may interact with and strengthen each other. Finally, we address the role of uncertainty in persuasion strategies and show how it can be represented in persuasion graphs.