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1.
《Information processing & management》2020,57(2):102034
Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures. 相似文献
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In this paper, we present a novel clustering algorithm to generate a number of candidate clusters from other web search results. The candidate clusters generate a connective relation among the clusters and the relation is semantic. Moreover, the algorithm also contains the following attractive properties: (1) it can be applied to multilingual web documents, (2) it improves the clustering performance of any search engine, (3) its unsupervised learning can automatically identify potentially relevant knowledge without using any corpus, and (4) clustering results are generated on the fly and fitted into search engines. 相似文献
3.
In this paper, we provide a new insight into clustering with a spring–mass dynamics, and propose a resulting hierarchical clustering algorithm. To realize the spectral graph partitioning as clustering, we model a weighted graph of a data set as a mass–spring dynamical system, where we regard a cluster as an oscillating single entity of a data set with similar properties. And then, we describe how oscillation modes are related with eigenvectors of a graph Laplacian matrix of the data set. In each step of the clustering, we select a group of clusters, which has the biggest number of constituent clusters. This group is divided into sub-clusters by examining an eigenvector minimizing a cost function, which is formed in such a way that subdivided clusters will be balanced with large size. To find k clusters out of non-spherical or complex data, we first transform the data into spherical clusters located on the unit sphere positioned in the (k−1)-dimensional space. In the sequel, we use the previous procedure to these transformed data. The computational experiments demonstrate that the proposed method works quite well on a variety of data sets, although its performance degrades with the degree of overlapping of data sets. 相似文献
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用户兴趣本体弥补了基于关键词的用户兴趣模型不能从语义上表达用户兴趣的缺陷,但大多是利用领域本体来构建,很难反映用户多方面和潜在兴趣,并且构建领域本体也是一个难点。本文据此提出一种基于词汇同现的用户兴趣本体构建方法。根据网页浏览记录找到用户兴趣网页集,经过数据处理将其转换成用户兴趣文本集。以TFIDF为指标抽取概念,词汇同现统计提取概念间关系,运用无尺度K-中心点聚类算法对其调整,将有关联用户的本体合并得到多用户本体,该本体能在语义上更全面反映用户兴趣并发现潜在兴趣。 相似文献
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This article presents conceptual navigation and NavCon, an architecture that implements this navigation in World Wide Web pages. NavCon architecture makes use of ontology as metadata to contextualize user search for information. Based on ontologies, NavCon automatically inserts conceptual links in Web pages. By using these links, the user may navigate in a graph representing ontology concepts and their relationships. By browsing this graph, it is possible to reach documents associated with the user desired ontology concept. This Web navigation supported by ontology concepts we call conceptual navigation. Conceptual navigation is a technique to browse Web sites within a context. The context filters relevant retrieved information. The context also drives user navigation through paths that meet his needs. A company may implement conceptual navigation to improve user search for information in a knowledge management environment. We suggest that the use of an ontology to conduct navigation in an Intranet may help the user to have a better understanding about the knowledge structure of the company. 相似文献
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Jean-François Pessiot Young-Min Kim Massih R. Amini Patrick Gallinari 《Information processing & management》2010
Most document clustering algorithms operate in a high dimensional bag-of-words space. The inherent presence of noise in such representation obviously degrades the performance of most of these approaches. In this paper we investigate an unsupervised dimensionality reduction technique for document clustering. This technique is based upon the assumption that terms co-occurring in the same context with the same frequencies are semantically related. On the basis of this assumption we first find term clusters using a classification version of the EM algorithm. Documents are then represented in the space of these term clusters and a multinomial mixture model (MM) is used to build document clusters. We empirically show on four document collections, Reuters-21578, Reuters RCV2-French, 20Newsgroups and WebKB, that this new text representation noticeably increases the performance of the MM model. By relating the proposed approach to the Probabilistic Latent Semantic Analysis (PLSA) model we further propose an extension of the latter in which an extra latent variable allows the model to co-cluster documents and terms simultaneously. We show on these four datasets that the proposed extended version of the PLSA model produces statistically significant improvements with respect to two clustering measures over all variants of the original PLSA and the MM models. 相似文献
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《Information processing & management》2023,60(3):103304
Person re-identification (ReID) based on heterogeneous data aims to search for the same pedestrian from different modalities. The existing unsupervised heterogeneous ReID method overly relies on pseudo labels and ignores the inter-image feature relationship. In the paper, we propose a novel Diversity Feature Constraint (DFC) method to simultaneously consider the clustering-level and instance-level feature relationship for unsupervised heterogeneous ReID. On the one hand, we employ the clustering algorithm to produce pseudo labels for heterogeneous images. Then, the clustering-level constraint is designed to optimize the model. On the other hand, considering that the clustering algorithm may generate some noise, we propose the complementary intra-modality instance-level constraint to correlate any two intra-modality images. Meanwhile, for eliminating the inter-modality discrepancy, the inter-modality instance-level constraint is developed to decrease the large inter-modality gap. We construct the potential feature relationship between heterogeneous images to constrain the feature distribution. By experiments, we prove that over-reliance on pseudo labels generates limited performance. Exploring inter-image potential relationships is an important way to solve the unsupervised problem. Extensive results demonstrate that DFC achieves superior performance that outperforms other methods by a large margin, improving 15.23% and 9.37% at rank-1 and mAP indexes compared with the clustering method on SYSU-MM01. 相似文献
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《Information processing & management》2020,57(6):102338
Node clustering on heterogeneous information networks (HINs) plays an important role in many real-world applications. While previous research mainly clusters same-type nodes independently via exploiting structural similarity search, they ignore the correlations of different-type nodes. In this paper, we focus on the problem of co-clustering heterogeneous nodes where the goal is to mine the latent relevance of heterogeneous nodes and simultaneously partition them into the corresponding type-aware clusters. This problem is challenging in two aspects. First, the similarity or relevance of nodes is not only associated with multiple meta-path-based structures but also related to numerical and categorical attributes. Second, clusters and similarity/relevance searches usually promote each other.To address this problem, we first design a learnable overall relevance measure that integrates the structural and attributed relevance by employing meta-paths and attribute projection. We then propose a novel approach, called SCCAIN, to co-cluster heterogeneous nodes based on constrained orthogonal non-negative matrix tri-factorization. Furthermore, an end-to-end framework is developed to jointly optimize the relevance measures and co-clustering. Extensive experiments on real-world datasets not only demonstrate that SCCAIN consistently outperforms state-of-the-art methods but also validate the effectiveness of integrating attributed and structural information for co-clustering. 相似文献
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To address the inability of current ranking systems to support subtopic retrieval, two main post-processing techniques of search results have been investigated: clustering and diversification. In this paper we present a comparative study of their performance, using a set of complementary evaluation measures that can be applied to both partitions and ranked lists, and two specialized test collections focusing on broad and ambiguous queries, respectively. The main finding of our experiments is that diversification of top hits is more useful for quick coverage of distinct subtopics whereas clustering is better for full retrieval of single subtopics, with a better balance in performance achieved through generating multiple subsets of diverse search results. We also found that there is little scope for improvement over the search engine baseline unless we are interested in strict full-subtopic retrieval, and that search results clustering methods do not perform well on queries with low divergence subtopics, mainly due to the difficulty of generating discriminative cluster labels. 相似文献
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We report our experience with a novel approach to interactive information seeking that is grounded in the idea of summarizing query results through automated document clustering. We went through a complete system development and evaluation cycle: designing the algorithms and interface for our prototype, implementing them and testing with human users. Our prototype acted as an intermediate layer between the user and a commercial Internet search engine (AltaVista), thus allowing searches of the significant portion of World Wide Web. In our final evaluation, we processed data from 36 users and concluded that our prototype improved search performance over using the same search engine (AltaVista) directly. We also analyzed effects of various related demographic and task related parameters. 相似文献
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Carmen De Maio Giuseppe Fenza Vincenzo Loia Sabrina Senatore 《Information processing & management》2012
In recent years, knowledge structuring is assuming important roles in several real world applications such as decision support, cooperative problem solving, e-commerce, Semantic Web and, even in planning systems. Ontologies play an important role in supporting automated processes to access information and are at the core of new strategies for the development of knowledge-based systems. Yet, developing an ontology is a time-consuming task which often needs an accurate domain expertise to tackle structural and logical difficulties in the definition of concepts as well as conceivable relationships. This work presents an ontology-based retrieval approach, that supports data organization and visualization and provides a friendly navigation model. It exploits the fuzzy extension of the Formal Concept Analysis theory to elicit conceptualizations from datasets and generate a hierarchy-based representation of extracted knowledge. An intuitive graphical interface provides a multi-facets view of the built ontology. Through a transparent query-based retrieval, final users navigate across concepts, relations and population. 相似文献
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《Information processing & management》2003,39(1):117-131
The inverted file is the most popular indexing mechanism for document search in an information retrieval system. Compressing an inverted file can greatly improve document search rate. Traditionally, the d-gap technique is used in the inverted file compression by replacing document identifiers with usually much smaller gap values. However, fluctuating gap values cannot be efficiently compressed by some well-known prefix-free codes. To smoothen and reduce the gap values, we propose a document-identifier reassignment algorithm. This reassignment is based on a similarity factor between documents. We generate a reassignment order for all documents according to the similarity to reassign closer identifiers to the documents having closer relationships. Simulation results show that the average gap values of sample inverted files can be reduced by 30%, and the compression rate of d-gapped inverted file with prefix-free codes can be improved by 15%. 相似文献
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《Information processing & management》2002,38(4):559-582
Hierarchic document clustering has been widely applied to information retrieval (IR) on the grounds of its potential improved effectiveness over inverted file search (IFS). However, previous research has been inconclusive as to whether clustering does bring improvements. In this paper we take the view that if hierarchic clustering is applied to search results (query-specific clustering), then it has the potential to increase the retrieval effectiveness compared both to that of static clustering and of conventional IFS. We conducted a number of experiments using five document collections and four hierarchic clustering methods. Our results show that the effectiveness of query-specific clustering is indeed higher, and suggest that there is scope for its application to IR. 相似文献
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Modern web search engines are expected to return the top-k results efficiently. Although many dynamic index pruning strategies have been proposed for efficient top-k computation, most of them are prone to ignoring some especially important factors in ranking functions, such as term-proximity (the distance relationship between query terms in a document). In our recent work [Zhu, M., Shi, S., Li, M., & Wen, J. (2007). Effective top-k computation in retrieving structured documents with term-proximity support. In Proceedings of 16th CIKM conference (pp. 771–780)], we demonstrated that, when term-proximity is incorporated into ranking functions, most existing index structures and top-k strategies become quite inefficient. To solve this problem, we built the inverted index based on web page structure and proposed the query processing strategies accordingly. The experimental results indicate that the proposed index structures and query processing strategies significantly improve the top-k efficiency. In this paper, we study the possibility of adopting additional techniques to further improve top-k computation efficiency. We propose a Proximity-Probe Heuristic to make our top-k algorithms more efficient. We also test the efficiency of our approaches on various settings (linear or non-linear ranking functions, exact or approximate top-k processing, etc.). 相似文献
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A query-relevant snippet for ontology search is useful for deciding if an ontology fits users’ needs. In this paper, we illustrate a good snippet in a keyword-based ontology search engine should be with term-association view and compact, and propose an approach to generate it. To obtain term-association view snippets, a model of term association graph for ontology is proposed, and a concept of maximal r-radius subgraph is introduced to decompose the term association graph into connected subgraphs, which preserve close relations between terms. To achieve compactness, in a query-relevant maximal r-radius subgraph, a connected subgraph thereof with a small graph weight is extracted as a sub-snippet. Finally, a greedy method is used to select sub-snippets to form a snippet in consideration of query relevance and compactness without violating the length constraint. An empirical study on our implementation shows that our approach is feasible. An evaluation on effectiveness shows that the term-association view snippet is favored by users, and the compactness helps reading and judgment. 相似文献
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基于本体（ontology）理论,利用本体对概念、术语及其相互关系的规范化描述,勾画出数字图书馆领域的基本知识体系,构建了数字图书馆领域本体库;然后,对现有的图书馆搜索引擎系统进行了改进,将构建的本体库引入到系统中,实现了基于本体的信息检索,方便用户对资源的查询,为用户提供高质量的个性化服务。 相似文献
19.
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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《Information processing & management》2001,37(4):593-601
There are several recent studies that propose search output clustering as an alternative representation method to ranked output. Users are provided with cluster representations instead of lists of titles and invited to make decisions on groups of documents. This paper discusses the difficulties involved in representing clusters for users’ evaluation in a concise but easily interpretable form. The discussion is based on findings and user feedback from a user study investigating the effectiveness of search output clustering. The overall impression created by the experiment results and users’ feedback is that clusters cannot be relied on to consistently produce meaningful document groups that can easily be recognised by the users. They also seem to lead to unrealistic user expectations. 相似文献