Neural embedding-based indices for semantic search     

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   

Organizational justice and management trustworthiness during organizational Change: Interactions of Benevolence,Integrity, and managerial approaches     

Yu Cui  Hao Jiao 《Information processing & management》2019,56(4):1526-1542

Trust and justice play an important role in the process of organizational change to build dynamic capabilities for sustainable competitive advantage. This study investigated the interaction effects of management's benevolence trustworthiness and integrity trustworthiness on employees’ perception of procedural justice during innovation or organizational change. Both the scenario and the field study showed that the patterns of the interaction effects of these two components of trustworthiness are further influenced in a complementary manner by different innovation approaches. The study indicated that the relationships between benevolence trustworthiness and integrity trustworthiness are far more complex than expected and thus need more research efforts.  相似文献   

A knowledge-based semantic framework for query expansion     

Jamal Abdul Nasir  Iraklis Varlamis  Samreen Ishfaq 《Information processing & management》2019,56(5):1605-1617

Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets.  相似文献   

Web document summarization by exploiting social context with matrix co-factorization     

Minh-Tien Nguyen  Viet Cuong Tran  Xuan Hoai Nguyen  Le-Minh Nguyen 《Information processing & management》2019,56(3):495-515

In the context of social media, users usually post relevant information corresponding to the contents of events mentioned in a Web document. This information posses two important values in that (i) it reflects the content of an event and (ii) it shares hidden topics with sentences in the main document. In this paper, we present a novel model to capture the nature of relationships between document sentences and post information (comments or tweets) in sharing hidden topics for summarization of Web documents by utilizing relevant post information. Unlike previous methods which are usually based on hand-crafted features, our approach ranks document sentences and user posts based on their importance to the topics. The sentence-user-post relation is formulated in a share topic matrix, which presents their mutual reinforcement support. Our proposed matrix co-factorization algorithm computes the score of each document sentence and user post and extracts the top ranked document sentences and comments (or tweets) as a summary. We apply the model to the task of summarization on three datasets in two languages, English and Vietnamese, of social context summarization and also on DUC 2004 (a standard corpus of the traditional summarization task). According to the experimental results, our model significantly outperforms the basic matrix factorization and achieves competitive ROUGE-scores with state-of-the-art methods.  相似文献   

Cognitive-inspired domain adaptation of sentiment lexicons     

Frank Z. Xing  Filippo Pallucchini  Erik Cambria 《Information processing & management》2019,56(3):554-564

Sentiment lexicons are essential tools for polarity classification and opinion mining. In contrast to machine learning methods that only leverage text features or raw text for sentiment analysis, methods that use sentiment lexicons embrace higher interpretability. Although a number of domain-specific sentiment lexicons are made available, it is impractical to build an ex ante lexicon that fully reflects the characteristics of the language usage in endless domains. In this article, we propose a novel approach to simultaneously train a vanilla sentiment classifier and adapt word polarities to the target domain. Specifically, we sequentially track the wrongly predicted sentences and use them as the supervision instead of addressing the gold standard as a whole to emulate the life-long cognitive process of lexicon learning. An exploration-exploitation mechanism is designed to trade off between searching for new sentiment words and updating the polarity score of one word. Experimental results on several popular datasets show that our approach significantly improves the sentiment classification performance for a variety of domains by means of improving the quality of sentiment lexicons. Case-studies also illustrate how polarity scores of the same words are discovered for different domains.  相似文献   

Meta-Circuit machine: Inferencing human collaborative relationships in heterogeneous information networks     

Xiyue Gao  Jun Chen  Nian Huai 《Information processing & management》2019,56(3):844-857

Human collaborative relationship inference is a meaningful task for online social networks and is called link prediction in network science. Real-world networks contain multiple types of interacting components and can be modeled naturally as heterogeneous information networks (HINs). The current link prediction algorithms in HINs fail to effectively extract training samples from snapshots of HINs; moreover, they underutilise the differences between nodes and between meta-paths. Therefore, we propose a meta-circuit machine (MCM) that can learn and fuse node and meta-path features efficiently, and we use these features to inference the collaborative relationships in question-and-answer and bibliographic networks. We first utilise meta-circuit random walks to obtain training samples in which the basic idea is to perform biased meta-path random walks on the input and target network successively and then connect them. Then, a meta-circuit recurrent neural network (mcRNN) is designed for link prediction, which represents each node and meta-path by a dense vector and leverages an RNN to fuse the features of node sequences. Experiments on two real-world networks demonstrate the effectiveness of our framework. This study promotes the investigation of potential evolutionary mechanisms for collaborative relationships and offers practical guidance for designing more effective recommendation systems for online social networks.  相似文献   

Online visibility of software-related web sites: The case of biomedical text mining tools     

Gael Pérez-Rodríguez  Martín Pérez-Pérez  Florentino Fdez-Riverola  Anália Lourenço 《Information processing & management》2019,56(3):565-583

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Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis     

Kai Hu  Qing Luo  Kunlun Qi  Siluo Yang  Jin Mao  Xiaokang Fu  Jie Zheng  Huayi Wu  Ya Guo  Qibing Zhu 《Information processing & management》2019,56(4):1185-1203

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.  相似文献   

论回复时限存在的不足及应对措施--关于维护学术期刊作者权利的思考     

任辉 《荆门职业技术学院学报》2004,19(2):87-90

稿约规则中关于回复时限的条款在某些方面存在着不足，在实际操作中易造成作者合法权利的损害。因此，修改回复时限、理顺出版者与作者间的权利和义务关系十分必要。解决这个问题，可从导致审稿时间过长的“瓶颈”——审稿过程入手，做到工作规范，审稿高效，退稿及时。  相似文献   

Factors Influencing Information Literacy Competency of College Students     

Scott Lanning  Jill Mallek 《The Journal of Academic Librarianship》2017,43(5):443-450

This paper analyzes multiple factors from current university students' high school experiences, including demographic, educational, and economic factors, and current standing and grade point average (GPA), to evaluate the students' information literacy skills associated with a 1000 level course on information literacy which is part of the university's general education requirement. The pre-test indicates that students lack sufficient skills needed to do college-level research. Results of regression analyses demonstrate that only current university GPA and standardized test scores have any influence on information literacy test scores.  相似文献