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We study several machine learning algorithms for cross-language patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, which basically used learning techniques for monolingual sub-tasks, our learning algorithms exploit the bilingual training documents and learn a semantic representation from them. We study Japanese–English cross-language patent retrieval using Kernel Canonical Correlation Analysis (KCCA), a method of correlating linear relationships between two variables in kernel defined feature spaces. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. We also investigate learning algorithms for cross-language document classification. The learning algorithm are based on KCCA and Support Vector Machines (SVM). In particular, we study two ways of combining the KCCA and SVM and found that one particular combination called SVM_2k achieved better results than other learning algorithms for either bilingual or monolingual test documents. 相似文献
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Fatemeh Lashkari Ebrahim Bagheri Ali A. Ghorbani 《Information processing & management》2019,56(3):733-755
Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus. 相似文献
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Through the recent NTCIR workshops, patent retrieval casts many challenging issues to information retrieval community. Unlike newspaper articles, patent documents are very long and well structured. These characteristics raise the necessity to reassess existing retrieval techniques that have been mainly developed for structure-less and short documents such as newspapers. This study investigates cluster-based retrieval in the context of invalidity search task of patent retrieval. Cluster-based retrieval assumes that clusters would provide additional evidence to match user’s information need. Thus far, cluster-based retrieval approaches have relied on automatically-created clusters. Fortunately, all patents have manually-assigned cluster information, international patent classification codes. International patent classification is a standard taxonomy for classifying patents, and has currently about 69,000 nodes which are organized into a five-level hierarchical system. Thus, patent documents could provide the best test bed to develop and evaluate cluster-based retrieval techniques. Experiments using the NTCIR-4 patent collection showed that the cluster-based language model could be helpful to improving the cluster-less baseline language model. 相似文献
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The number of patent documents is currently rising rapidly worldwide, creating the need for an automatic categorization system to replace time-consuming and labor-intensive manual categorization. Because accurate patent classification is crucial to search for relevant existing patents in a certain field, patent categorization is a very important and useful field. As patent documents are structural documents with their own characteristics distinguished from general documents, these unique traits should be considered in the patent categorization process. In this paper, we categorize Japanese patent documents automatically, focusing on their characteristics: patents are structured by claims, purposes, effects, embodiments of the invention, and so on. We propose a patent document categorization method that uses the k-NN (k-Nearest Neighbour) approach. In order to retrieve similar documents from a training document set, some specific components to denote the so-called semantic elements, such as claim, purpose, and application field, are compared instead of the whole texts. Because those specific components are identified by various user-defined tags, first all of the components are clustered into several semantic elements. Such semantically clustered structural components are the basic features of patent categorization. We can achieve a 74% improvement of categorization performance over a baseline system that does not use the structural information of the patent. 相似文献
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《Information processing & management》2005,41(5):1065-1080
Traditional index weighting approaches for information retrieval from texts depend on the term frequency based analysis of the text contents. A shortcoming of these indexing schemes, which consider only the occurrences of the terms in a document, is that they have some limitations in extracting semantically exact indexes that represent the semantic content of a document. To address this issue, we developed a new indexing formalism that considers not only the terms in a document, but also the concepts. In this approach, concept clusters are defined and a concept vector space model is proposed to represent the semantic importance degrees of lexical items and concepts within a document. Through an experiment on the TREC collection of Wall Street Journal documents, we show that the proposed method outperforms an indexing method based on term frequency (TF), especially in regard to the few highest-ranked documents. Moreover, the index term dimension was 80% lower for the proposed method than for the TF-based method, which is expected to significantly reduce the document search time in a real environment. 相似文献
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本文在简要地介绍了关键词检索的现状之后,重点从文献检索的角度分析了专利文献特点,并探讨了完善关键词检索的3个方面。最后,就专利文献检索领域关键词检索的发展趋势进行了简要的分析。 相似文献
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与传统经营风险相比,专利诉讼风险尤其是专利侵权诉讼风险对企业的危害更大。为保障我国专利司法保护与市场主体的科技创新,以1994－2020年中国知网数据库关于专利诉讼的383篇核心文献为对象,运用CiteSpace软件分析国内专利诉讼研究热点、演进趋势,并对前沿进行分析和预判。结果发现：国内早期相关研究从2004年开始增多、2016年达到顶峰,受政策与热点案件影响较大,研究脉络分为3个阶段,内容主要包括专利诉讼的基础理论、专利战略、非专利实施主体与专利诉讼信息运用等,热点以专利诉讼、专利侵权诉讼、专利侵权三大主题为中心向外发散,研究视角较多,但核心作者群尚未形成。进而提出在专利诉讼领域搭建适合于我国的相关理论研究框架、加强其他各领域的合作研究,企业强化专利风险管理和专利诉讼信息分析利用,并重点关注企业在出海过程中可能遇到的专利诉讼的应对措施等。 相似文献
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This paper proposes a method to improve retrieval performance of the vector space model (VSM) in part by utilizing user-supplied information of those documents that are relevant to the query in question. In addition to the user's relevance feedback information, information such as original document similarities is incorporated into the retrieval model, which is built by using a sequence of linear transformations. High-dimensional and sparse vectors are then reduced by singular value decomposition (SVD) and transformed into a low-dimensional vector space, namely the space representing the latent semantic meanings of words. The method has been tested with two test collections, the Medline collection and the Cranfield collection. In order to train the model, multiple partitions are created for each collection. Improvement of average precision of the averages over all partitions, compared with the latent semantic indexing (LSI) model, are 20.57% (Medline) and 22.23% (Cranfield) for the two training data sets, and 0.47% (Medline) and 4.78% (Cranfield) for the test data, respectively. The proposed method provides an approach that makes it possible to preserve user-supplied relevance information for the long term in the system in order to use it later. 相似文献
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Harold Borko 《Information processing & management》1977,13(6):355-365
A theory of indexing helps explain the nature of indexing, the structure of the vocabulary, and the quality of the index. Indexing theories formulated by Jonker, Heilprin, Landry and Salton are described. Each formulation has a different focus. Jonker, by means of the Terminological and Connective Continua, provided a basis for understanding the relationships between the size of the vocabulary, the hierarchical organization, and the specificity by which concepts can be described. Heilprin introduced the idea of a search path which leads from query to document. He also added a third dimension to Jonker's model; the three variables are diffuseness, permutivity and hierarchical connectedness. Landry made an ambitious and well conceived attempt to build a comprehensive theory of indexing predicated upon sets of documents, sets of attributes, and sets of relationships between the two. It is expressed in theorems and by formal notation. Salton provided both a notational definition of indexing and procedures for improving the ability of index terms to discriminate between relevant and nonrelevant documents. These separate theories need to be tested experimentally and eventually combined into a unified comprehensive theory of indexing. 相似文献
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An integrated information retrieval system generally contains multiple databases that are inconsistent in terms of their content and indexing. This paper proposes a rough set-based transfer (RST) model for integration of the concepts of document databases using various indexing languages, so that users can search through the multiple databases using any of the current indexing languages. The RST model aims to effectively create meaningful transfer relations between the terms of two indexing languages, provided a number of documents are indexed with them in parallel. In our experiment, the indexing concepts of two databases respectively using the Thesaurus of Social Science (IZ) and the Schlagwortnormdatei (SWD) are integrated by means of the RST model. Finally, this paper compares the results achieved with a cross-concordance method, a conditional probability based method and the RST model. 相似文献
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以专利标准化为切入点,基于专利标准化演化的特点,将其分为,专利角逐标准、专利影响标准、专利占领标准、专利垄断标准四个演化阶段。并尝试从技术、市场、管理、政策、法律五大层面分析与探讨专利在标准化演化进程中的存在的核心问题,以期为专利标准化的顺利演化提供一定的理论指导。 相似文献
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一种智能型的信息检索方法:隐含语义索引法 总被引：3，自引：0，他引：3
介绍了一种新的自动索引和检索方法——隐含语义索引法。隐含语义索引法是一种全自动的智能索引方法，通过挖掘文本与词汇之间的隐含关系来达到提高检索效率的目的。 相似文献
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基于本体的文本信息检索研究 总被引：5，自引：0，他引：5
本文对如何构建基于本体的文本信息检索系统进行了探讨．并认为，利用反映概念之间关系的领域本体指导主题标引，利用反映实体之间关系的领域本体指导实体关系标引，并以本体的形式表示文档替代物和查询表达式，可以进一步提高文本信息检索系统的性能。 相似文献
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《Information processing & management》2002,38(3):305-323
We compare support vector machines (SVMs) to Rocchio, Ide regular and Ide dec-hi algorithms in information retrieval (IR) of text documents using relevancy feedback. It is assumed a preliminary search finds a set of documents that the user marks as relevant or not and then feedback iterations commence. Particular attention is paid to IR searches where the number of relevant documents in the database is low and the preliminary set of documents used to start the search has few relevant documents. Experiments show that if inverse document frequency (IDF) weighting is not used because one is unwilling to pay the time penalty needed to obtain these features, then SVMs are better whether using term-frequency (TF) or binary weighting. SVM performance is marginally better than Ide dec-hi if TF-IDF weighting is used and there is a reasonable number of relevant documents found in the preliminary search. If the preliminary search is so poor that one has to search through many documents to find at least one relevant document, then SVM is preferred. 相似文献
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Antonio Jimeno-Yepes Rafael Berlanga-Llavori Dietrich Rebholz-Schuhmann 《Information processing & management》2010
Ontologies are frequently used in information retrieval being their main applications the expansion of queries, semantic indexing of documents and the organization of search results. Ontologies provide lexical items, allow conceptual normalization and provide different types of relations. However, the optimization of an ontology to perform information retrieval tasks is still unclear. In this paper, we use an ontology query model to analyze the usefulness of ontologies in effectively performing document searches. Moreover, we propose an algorithm to refine ontologies for information retrieval tasks with preliminary positive results. 相似文献