排序方式： 共有33条查询结果，搜索用时 31 毫秒
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针对汉语框架网络本体（CFN）在词汇覆盖面及语义分析和推理中存在的不足,在充分分析其结构的基础上,通过将其与另外两大知识库WordNet和VerbNet的集成,以增强汉语框架网络本体的广度和深度,从而达到构建一个功能强大的汉语框架网络本体知识库的目的。 相似文献
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基于语义网计算英语词语相似度 总被引：14，自引：2，他引：14
本文介绍一种基于WordNet的计算英语词语相似度的实现方法:从WordNet中提取同义词并采取向量空间方法计算英语词语的相似度。向量包括三方面:(1)WordNet的同义词词集(Synset),(2)类属信息(Class),(3)意义解释(Sense explanation)。实验结果表明,这是计算英语词语相似度的一种可行的方法。 相似文献
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Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome. 相似文献
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VerbNet是国际上近年构建的一个崭新的语义网络资源，作为计算机的动词词典，它克服了WordNet（词网）和Levin动词分类系统的一些限制，把动词词义分为几个大的范畴，为计算机语义理解提供了更好更精确的语义网络资源。 相似文献
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This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described. 相似文献
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Diego Reforgiato Recupero 《Information Retrieval》2007,10(6):563-579
Text document clustering provides an effective and intuitive navigation mechanism to organize a large amount of retrieval
results by grouping documents in a small number of meaningful classes. Many well-known methods of text clustering make use
of a long list of words as vector space which is often unsatisfactory for a couple of reasons: first, it keeps the dimensionality
of the data very high, and second, it ignores important relationships between terms like synonyms or antonyms. Our unsupervised
method solves both problems by using ANNIE and WordNet lexical categories and WordNet ontology in order to create a well structured
document vector space whose low dimensionality allows common clustering algorithms to perform well. For the clustering step
we have chosen the bisecting k-means and the Multipole tree, a modified version of the Antipole tree data structure for, respectively, their accuracy and
speed.
相似文献
| Diego Reforgiato RecuperoEmail: |
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邵晓霞 《成都教育学院学报》2009,23(3):86-88
英语词汇是英语学习中很重要的内容,然而英语词汇学习却总是为“费时低效”的境况所困扰。在大学英语教改的浪潮下,尝试借助其他领域现有的研究成果来提高英语词汇教学效果或许是一条出路。文章通过对概念图、WordNet的主要特征以及英语词汇的存储与习得心理过程的简要分析,探讨利用WordNet及概念图提高英语词汇习得的可能性。 相似文献
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Text Categorization (TC) is the automated assignment of text documents to predefined categories based on document contents. TC has been an application for many learning approaches, which prove effective. Nevertheless, TC provides many challenges to machine learning. In this paper, we suggest, for text categorization, the integration of external WordNet lexical information to supplement training data for a semi-supervised clustering algorithm which can learn from both training and test documents to classify new unseen documents. This algorithm is the Semi-Supervised Fuzzy c-Means (ssFCM). Our experiments use Reuters 21578 database and consist of binary classifications for categories selected from the 115 TOPICS classes of the Reuters collection. Using the Vector Space Model, each document is represented by its original feature vector augmented with external feature vector generated using WordNet. We verify experimentally that the integration of WordNet helps ssFCM improve its performance, effectively addresses the classification of documents into categories with few training documents and does not interfere with the use of training data. 相似文献
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Word sense disambiguation is important in various aspects of natural language processing, including Internet search engines, machine translation, text mining, etc. However, the traditional methods using case frames are not effective for solving context ambiguities that requires information beyond sentences. This paper presents a new scheme for solving context ambiguities using a field association scheme. Generally, the scope of case frames is restricted to one sentence; however, the scope of the field association scheme can be applied to a set of sentences. In this paper, a formal disambiguation algorithm is proposed to control the scope for a set of variable number of sentences with ambiguities as well as solve ambiguities by calculating the weight of fields. In the experiments, 52 English and 20 Chinese words are disambiguated by using 104,532 Chinese and 38,372 English field association terms. The accuracy of the proposed field association scheme for context ambiguities is 65% higher than the case frame method. The proposed scheme shows better results than other three known methods, namely UNED-LS-U, IIT-2, and Relative-based in corpus SENSEVAL-2. 相似文献