认知语言学视角下的多义语语义扩张——以日语动词“行く”为例

多义语的语义扩张是按照一定的规则和模式来进行的。动词"行く"的各语义所组成的义群结构关系反映出多义语语义扩张的三种模式为"隐喻"、"换喻"以及"提喻"。语义扩张具有明显的方向性,即由空间概念向时间概念、由具体概念向抽象概念以及由客观概念向主观概念的语义扩张。在语义扩张过程的各环节中,"变化"和"认同"是起关键作用的环节。人类具有的创造性的认知能力、文化间的交流与碰撞以及知觉的选择性是多义语语义扩张的内在原因及推动力。

认知语言学视角下的多义语语义扩展——以日语动词“KAESU”为例

本文从认知语义学角度出发,运用籾山洋介(2002)的语义扩展模式,分析了日语动词“KAESU”的多义扩展情况,明确了“KAESU”的原型义是“翻转”,描述了原型义与各个义项之间的语义关系以及其语义扩展机制,构建了“KAESU”的多义扩展网络图。

谈海明威作品中多义词语的翻译

谈海明威作品中多义词语的翻译刘振前海明威是美国２０世纪最著名的小说家之一。其作品风格质朴、清新、客观，很少在作品中流露自己的思想、感情与态度，被形象地称为＂冰山式＂风格。这种风格加大了读者与作者之间的距离，缩短了作品与读者之间的距离，要求读者在阅读过...

广告语的词语修辞手段的运用

广告语言实际上是“信息传递艺术”，而调动各种词语的修辞手段，例如反义词语的运用，对举鲜明；多义、同音词语的巧用，一语双关；古语词的使用，庄重凝练；方言词、外来词的慎用，增强表现力；变异形式的创用，别具一格等等。则是实现广告语言艺术性的关键。

Auto-expanded multi query examples technology in content-based image retrieval

In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.

多義動詞「カケル」の学習についての一考察——話し言葉における日本語母語話者の使用実態を踏まえて

日本語母語話者の話し言葉における多義動詞「カケル」の使用実態を、3種の談話資料を用いて分析し、使用頻度の高い語義、共起頻度の高いコロケーションを特定した。その分析を踏まえ、「まずは使い勝手のよい具体的な用法を学習し、その捉え方と用法間の関連性を理解することにより応用のきく語義の知識とし、ネットワークを構築していく」という「カケル」の学習法を考案し、レベルに合った部分的ネットワークの構築を促す必要があることを述べた。

Multi-broker architecture for large-scale dynamic heterogeneous information integration

In order to realize interoperability to a large number of autonomous and heterogeneous information sources with high efficiency, an agent-based multi-broker architecture （AMA）-HustEven, is constructed. A group of broker agents are designed to provide brokering services in a peer-to-peer （P2P） manner for the non- broker agents （user agents, resource agents, query agents）. Thus, the scalability and robustness of the system are enhanced. Ontology is also used by the broker agents for facilitating interoperability among all the agents in HustEven. Unlike any other AMAs, an interdomain ontology is built in this system to represent the relationships among the common concepts in the innerdomain ontologies. Therefore, a broker forwards the queries only to the other related brokers according to the interdomain ontology and the communication overhead among the brokers is reduced. Obviously, the application of the interdomain ontology enables a broker to fully take advantage of the multi-broker architecture. The experimental results show that the HustEven performs more efficiently than any other existing systems.

Analysis of multimodality in PPT teaching discourse

Computer technology-based PPT is usually conceived as a tool for information transmission and presentation rather than as a type of discourse. Much focus of the previous study on PPT is concerned with its development, design and application. However, PPT itself may actually be regarded as a multimodal discourse comprising multisemiotics, such as linguistic signs, image, graph, sound, color and their interrelated layouts, etc.. So the article attempts to make a multimodal analysis of College English PPT discourse via the principle of reading images by Kress and van Leeuwen in 1996, aiming to present a different angle of interpreting the meaning of composition anchored in PPT.

Detecting abnormalities for empty nest elder in smart monitoring system

In order to implement the real-time detection of abnormality of elder and devices in an empty nest home,multi-modal joint sensors are used to collect discrete action sequences of behavior,and the improved hierarchical hidden Markov model is adopted to Abstract these discrete action sequences captured by multi-modal joint sensors into an occupant’s high-level behavior—event,then structure representation models of occupant normality are modeled from large amounts of spatio-temporal data. These models are used as classifiers of normality to detect an occupant’s abnormal behavior.In order to express context information needed by reasoning and detection,multi-media ontology （MMO） is designed to annotate and reason about the media information in the smart monitoring system.A pessimistic emotion model （PEM） is improved to analyze multi-interleaving events of multi-active devices in the home.Experiments demonstrate that the PEM can enhance the accuracy and reliability for detecting active devices when these devices are in blind regions or are occlusive. The above approach has good performance in detecting abnormalities involving occupants and devices in a real-time way.

Approximate Failures Semantics for Polynomial Labelled Transition Systems

Labelled transition systems(LTSs) are widely used to formally describe system behaviour.The labels of LTS are extended to offer a more satisfactory description of behaviour by refining the abstract labels into multivariate polynomials.These labels can be simplified by numerous numerical approximation methods.Those LTSs that can not apply failures semantics equivalence in description and verification may have a chance after using approximation on labels.The technique that combines approximation and failures semantics equivalence effectively alleviates the computational complexity and minimizes LTS.

Prototypical motivation of polysemy

This paper applies prototype theory to explain the motivation of polysemy. There are mainly 3 types of meaning model of polysemy, namely, radiation, concatenation and integrated model. According to prototype theory, in the semantic category formed by a polysemic word, category members are determined by prototype. They are the result of development from prototype to boundary. Connected by a network of overlapping similarities （i.e., family resemblances）, category members present different degrees of prototypicality, but not all of them can represent the category. Only the prototype can fully embody the category. With the extension of the semantic category, the boundary of the category is fuzzy and begins to intersect another semantic category.

Adapting Property Path for Polynomial-Time Evaluation and Reasoning on Semantic Web

Property path is the latest navigational extension of the standard query language SPARQL 1.1 for the Semantic Web.However,in the existing SPARQL query systems which support property path,the query efficiency is very low and does not support reasoning.This paper proposes a new existential semantics which has polynomial-time evaluation complexity and an equivalent relationship with the current semantics,and transforms the property path expressions to the extended nested regular expressions based on the existential semantics and proves the semantic equivalence after the transformation considering the RDFS semantics.The property path query engine is achieved by implementing the nested regular expressions algorithm and the transformation rules from the property path expressions to the nested regular expressions,which maintains the syntax simplicity of property path and the goal-oriented polynomial-time reasoning to avoid computing the RDF graph closure.The experiment results not only show the characteristics of query engine based on the existential semantics in efficiency and reasoning,but also further validate the equivalence between the results based on current semantics and those based on the existential semantics for property path after the removal of duplicate values.

Video Concept Detection Based on Multiple Features and Classifiers Fusion

The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the problem of semantic gap that low level features extracted by computers always fail to coincide with high-level concepts interpreted by humans. In this paper, we present a generic scheme for the detection video semantic concepts based on multiple visual features machine learning. Various global and local low-level visual features are systelrtically investigated, and kernelbased learning method equips the concept detection system to explore the potential of these features. Then we combine the different features and sub-systen on both classifier-level and kernel-level fusion that contribute to a more robust system Our proposed system is tested on the TRECVID dataset. The resulted Mean Average Precision （MAP） score is rmch better than the benchmark perforrmnce, which proves that our concepts detection engine develops a generic model and perforrrs well on both object and scene type concepts.

A novel algorithm for establishing taxonomic relation of Chinese ontology

Both a general domain-independent bottom-up multi-level model and an algorithm for establishing the taxonomic relation of Chinese ontology are proposed.The model consists of extracting domain vocabularies and establishing taxonomic relation,with the consideration of characteristics unique to Chinese natural language.By establishing the semantic forests of domain vocabularies and then using the existing semantic dictionary or machine-readable dictionary(MRD),the proposed algorithm can integrate these semantic forests together to establish the taxonomic relation.Experimental results show that the proposed algorithm is feasible and effective in establishing the integrated taxonomic relation among domain vocabularies and concepts.

一语多义的歇后语

2004年语文高考中将增加对熟语的考查。歇后语作为熟语的一部分，是一种特殊的语言形式，它是由前一部分“语”和后一部分“义”组合而成的．前一部分是个比喻或隐语，后一部分是意义的解释或者可以说，像一条谜语，前一部分是谜面，后一部分是谜底然而，这前后两部分的“谜面”和“谜底”

Evaluation of Multistrategy Classifiers for Heterogeneous Ontology Matching On the Semantic Web

On the semantic web, data interoperability and ontology heterogeneity are becoming ever more important issues. To resolve these problems, multiple classification methods can be used to learn the matching between ontologies. The paper uses the general statistic classification method to discover category features in data instances and use the first-order learning algorithm FOIL to exploit the semantic relations among data instances. When using multistrategy learning approach, a central problem is the evaluation of multistrategy classifiers. The goal and the conditions of using multistrategy classifiers within ontology matching are different from the ones for general text classification. This paper describes the combination rule of multiple classifiers called the Best Outstanding Champion, which is suitable for heterogeneous ontology mapping. On the prediction results of individual methods, the method can well accumulate the correct matching of alone classifier. The experiments show that the approach achieves high accuracy on real-world domain.

Semantic-based dynamic positioning mechanism for problem solving in multi-agent systems

In multi-agent systems(MAS),finding agents which are able to service properly in an open and dynamic environment are the key issue in problem solving.However,it is difficult to find agent resources quickly and position agents accurately and complete the system integration by the keyword matching method,due to the lack of clear semantic information of the classical agent model.An semantic-based agent dynamic positioning mechanism was proposed to assist in the system dynamic integration.According to the semantic agent model and the description method,a two-stage process including the domain positioning stage and the service semantic matching positioning stage,was discussed.With this mechanism,proper agents that provide appropriate service to assign sub-tasks for task completion can be found quickly and accurately.Finally,the effectiveness of the positioning mechanism was validated through the in-depth performance analysis in the application of simulation experiments to the system dynamic integration.