Graph-based Ranking Algorithm Research Articles

Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification

The increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on Arabic text summarization has not been sufficiently conducted. This study proposes an extractive Arabic review summarization approach that incorporates the reviews’ polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank. We demonstrate the advantages of the proposed methods through a set of experiments using hotel reviews from Booking.com. Reviews were grouped based on their polarity, and then TextRank was applied to produce the summary. Results were evaluated using two primary measures, BLEU and ROUGE. Further, two Arabic native speakers’ summaries were used for evaluation purposes. The results showed that this approach improved the summarization scores in most experiments, reaching an F1 score of 0.6294. Contributions of this work include applying a graph-based approach to a new domain, Arabic hotel reviews, adding sentiment dimension to summarization, analyzing the algorithms of the two primary summarization metrics showing the working of these measures and how they could be used to give accurate results, and finally, providing four human summaries for two hotels which could be utilized for another research.

AN IMPROVED TERM WEIGHTING AND DOCUMENT RANKING METHOD USING RANDOM WALK MODEL FOR INFORMATION RETRIEVAL

Document representation is one of the most fundamental issues in information retrieval application. The graph-based ranking algorithms represent document as a graph. Once a document is represented as graph, the similarity of that document to a query can be calculated in various ways and the calculation provides ranking to documents. This paper introduces an improved random-walk method to rank a document by considering position of a term within a document and information gain of that term within the whole document set. The experiments on various collection sets show that our approach improves the recall and precision than other proposed methods.

A Bregman Divergence Optimization Framework for Ranking on Data Manifold and Its New Extensions

Recently, graph-based ranking algorithms have received considerable interests in machine learning, computer vision and information retrieval communities. Ranking on data manifold (or manifold ranking, MR) is one of the representative approaches. One of the limitations of manifold ranking is its high computational complexity (O(n3), where n is the number of samples in database). In this paper, we cast the manifold ranking into a Bregman divergence optimization framework under which we transform the original MR to an equivalent optimal kernel matrix learning problem.With this new formulation, two effective and efficient extensions are proposed to enhance the ranking performance. Extensive experimental results on two real world image databases show the effectiveness of the proposed approach.

Family Rank: a graphical domain knowledge informed feature ranking algorithm.

When designing prediction models built with many features and relatively small sample sizes, feature selection methods often overfit training data, leading to selection of irrelevant features. One way to potentially mitigate overfitting is to incorporate domain knowledge during feature selection. Here, a feature ranking algorithm called 'Family Rank' is presented in which features are ranked based on a combination of graphical domain knowledge and feature scores computed from empirical data. A simulated dataset is used to demonstrate a scenario in which family rank outperforms other state-of-the-art graph based ranking algorithms, decreasing the sample size needed to detect true predictors by 2- to 3-fold. An example from oncology is then used to explore a real-world application of family rank. An implementation of Family Rank is freely available at https://cran.r-project.org/package=FamilyRank. Supplementary data are available at Bioinformatics online.

A Pattern Driven Graph Ranking Approach to Attribute Extraction for Knowledge Graph

Attribution extraction refers to find the attributes for the instances of a given semantic class, which is essential to enhance the schema of a knowledge graph. To facilitate the attribution extraction from the query log, this article proposes a pattern driven graph ranking approach to jointly employ the pattern and context distribution information. First, a simple pattern on query text is applied to automatically acquire seed attributes. Then, a graph-based weight propagation is designed to rank the patterns by context distribution algorithm information. Experimental results show that, on a Chinese query log collected by Baidu, the automatically acquired seeds are more representative than the classical manually assembled seeds, achieving an improvement of 11.6% in MAP as compared to the baseline approach. And the graph-based ranking algorithm manipulates the two types of evidence more effectively, outperforming both the distributional similarity based baseline and the HITS algorithm by 29.2% and 11.3%, respectively.

Indonesian sentiment summarization for lecturer learning evaluation by using textrank algorithm

Learning evaluation is a very important process to assess lecturer performances. Evaluation is carried out at the end of the semester by students. Student can give comments, critics, suggestions related to the subject taught by the lecturers. To analyse student comments, the lecturer can read the entire comments one by one. The main focus in this research was to develop the system that will be applied in summarizing student comments on the implementation of course learning. The algorithm that was used to perform text summarization was the TextRank algorithm. TextRank is a graph-based ranking algorithm (a graph with a ranking model) for processing text from natural or human language documents. We used 100 data sentiments by students to lecturers who teach one course in one semester. It was directly taken from SIAK (academic information system) by using web services. Based on the evaluation of the summaries by the expert, it was found that the textRank algorithm is quite representative in performing summarization for sentiments with the accuracy of 82%.

Text document clustering using statistical integrated graph based sentence sensitivity ranking algorithm

The proposed methodology employs a novel statistical integrated graph-based sentence sensitivity ranking algorithm for text document clustering. Clustering of documents is a task of grouping a document automatically into a list of meaningful clusters; in order for the documents inside a group to share the same topic. In this paper, first, a novel integrated graph-based methodology using the sentence sensitivity ranking is proposed to extract keyphrases from the documents. In the standard statistical approach, keyphrases are extracted on the basis of the sentence sensitivity ranking; and in the graph-based method, the candidate keyphrases are automatically created as graphs by applying the sentence sensitivity ranking. With the aid of the top listed keyphrases, the documents clustering are carried out by implementing the proposed sentence sensitivity ranking algorithm. The simulation results reveal that the proposed graph-based text document clustering using statistical integrated graph-based sentence sensitivity ranking algorithm obtained the best results for clustering the text documents.

Auditing the Sensitivity of Graph-based Ranking with Visual Analytics.

Graph mining plays a pivotal role across a number of disciplines, and a variety of algorithms have been developed to answer who/what type questions. For example, what items shall we recommend to a given user on an e-commerce platform? The answers to such questions are typically returned in the form of a ranked list, and graph-based ranking methods are widely used in industrial information retrieval settings. However, these ranking algorithms have a variety of sensitivities, and even small changes in rank can lead to vast reductions in product sales and page hits. As such, there is a need for tools and methods that can help model developers and analysts explore the sensitivities of graph ranking algorithms with respect to perturbations within the graph structure. In this paper, we present a visual analytics framework for explaining and exploring the sensitivity of any graph-based ranking algorithm by performing perturbation-based what-if analysis. We demonstrate our framework through three case studies inspecting the sensitivity of two classic graph-based ranking algorithms (PageRank and HITS) as applied to rankings in political news media and social networks.

Text Summarization using Extractive and Abstractive Methods

Text Summarization is a process where a huge text file is converted into summarized version which will preserve the original meaning and context. The main aim of any text summarization is to provide a accurate and precise summary. One approach is to use a sentence ranking algorithm. This comes under extractive summarization. Here, a graph based ranking algorithm is used to rank the sentences in the text and then top k-scored sentences are included in the summary. The most widely used algorithm to decide the importance of any vertex in a graph based on the information retrieved from the graph is Graph Based Ranking Algorithm. TextRank is one of the most efficient ranking algorithms which is used for Web link analysis that is for measuring the importance of website pages. Another approach is abstractive summarization where a LSTM encoder decoder model is used along with attention mechanism which focuses on some important words from the input. Encoder encodes the input sequence and decoder along with attention mechanism gives the summary as the output.

Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm.

With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.

Deep learning based QoE evaluation for internet video

The issue of quality of experience (QoE) evaluation in Internet video is challenging for mainly two facts. One is the sophisticated interactions between features from different entities: user, system and context. The other is the heterogeneity of feature type, i.e., sequential and non-sequential characteristics of features. To address above challenges, we propose a hybrid network model that integrates a deep neural network (DNN) and an improved recurrent neural network (RNN) for representation learning of view-level QoE evaluation. Non-sequential side information and time difference of sequential features are incorporated to different layers of RNN. Attention mechanism is applied for further improvement of RNN. Based on the output of attention network, we propose a graph-based ranking algorithm to conduct user behavior analyzing, which is useful to online decision of service providers. We conduct experiments on a real-world dataset from a large-scale VoD services provider. The results demonstrate that our method is more effective over the state-of-the-art methods for QoE evaluation.

Query-oriented text summarization based on hypergraph transversals

The rise in the amount of textual resources available on the Internet has created the need for tools of automatic document summarization. The main challenges of query-oriented extractive summarization are (1) to identify the topics of the documents and (2) to recover query-relevant sentences of the documents that together cover these topics. Existing graph- or hypergraph-based summarizers use graph-based ranking algorithms to produce individual scores of relevance for the sentences. Hence, these systems fail to measure the topics jointly covered by the sentences forming the summary, which tends to produce redundant summaries. To address the issue of selecting non-redundant sentences jointly covering the main query-relevant topics of a corpus, we propose a new method using the powerful theory of hypergraph transversals. First, we introduce a new topic model based on the semantic clustering of terms in order to discover the topics present in a corpus. Second, these topics are modeled as the hyperedges of a hypergraph in which the nodes are the sentences. A summary is then produced by generating a transversal of nodes in the hypergraph. Algorithms based on the theory of submodular functions are proposed to generate the transversals and to build the summaries. The proposed summarizer outperforms existing graph- or hypergraph-based summarizers by at least 6% of ROUGE-SU4 F-measure on DUC 2007 dataset. It is moreover cheaper than existing hypergraph-based summarizers in terms of computational time complexity.

Research on Gesture Recognition Based on Improved GBMR Segmentation and Multiple Feature Fusion

Aiming at addressing the problem of interactive gesture recognition between lunar robot and astronaut, a novel gesture detection and recognition algorithm is proposed. In gesture detection stage, based on saliency detection via Graph-Based Manifold Ranking (GBMR) algorithm, the depth information of foreground is added to the calculation of superpixel. By increasing the weight of connectivity domains in graph theory model, the foreground boundary is highlighted and the impact of background is weakened. In gesture recognition stage, Pyramid Histogram of Oriented Gradient (PHOG) feature and Gabor amplitude also phase feature of image samples are extracted. To highlight the Gabor amplitude feature, we propose a novel feature calculation by fusing feature in different directions at the same scale. Because of the strong classification capability and not-easy-to-fit advantage of Adaboosting, this paper applies it as the classifier to realize gesture recognition. Experimental results show that the improved gesture detection algorithm can maintain the robustness to influences of complex environment. Based on multi-feature fusion, the error rate of gesture recognition remains at about 4.2%, and the recognition rate is around 95.8%.

Can deep learning techniques improve classification performance of vandalism detection in Wikipedia?

Wikipedia is a free encyclopedia created as an international collaborative project. One of its peculiarities is that any user can edit its contents almost without restrictions, what has given rise to a phenomenon known as vandalism. Vandalism is any attempt that seeks to damage the integrity of the encyclopedia deliberately. To address this problem, in recent years several automatic detection systems and associated features have been developed. This work implements one of these systems, which uses three sets of new features based on different techniques. Specifically we study the applicability of a leading technology as deep learning to the problem of vandalism detection. The first set is obtained by expanding a list of vandal terms taking advantage of the existing semantic-similarity relations in word embeddings and deep neural networks. Deep learning techniques are applied to the second set of features, specifically Stacked Denoising Autoencoders (SDA), in order to reduce the dimensionality of a bag of words model obtained from a set of edits taken from Wikipedia. The last set uses graph-based ranking algorithms to generate a list of vandal terms from a vandalism corpus extracted from Wikipedia. These three sets of new features are evaluated separately as well as together to study their complementarity, improving the results in the state of the art. The system evaluation has been carried out on a corpus extracted from Wikipedia (WP_Vandal) as well as on another called PAN-WVC-2010 that was used in a vandalism detection competition held at CLEF conference.

An Integrated Graph Model for Document Summarization

Extractive summarization aims to produce a concise version of a document by extracting information-rich sentences from the original texts. The graph-based model is an effective and efficient approach to rank sentences since it is simple and easy to use. However, its performance depends heavily on good text representation. In this paper, an integrated graph model (iGraph) for extractive text summarization is proposed. An enhanced embedding model is used to detect the inherent semantic properties at the word level, bigram level and trigram level. Words with part-of-speech (POS) tags, bigrams and trigrams were extracted to train the embedding models. Based on the enhanced embedding vectors, the similarity values between the sentences were calculated in three perspectives. The sentences in the document were treated as vertexes and the similarity between them as edges. As a result, three different types of semantic graphs were obtained for every document, with the same nodes and different edges. These three graphs were integrated into one enriched semantic graph in a naive Bayesian fashion. After that, TextRank, which is a graph-based ranking algorithm, was applied to rank the sentences, before the top scored sentences were selected for the summary according to the compression rate. Evaluated on the DUC 2002 and DUC 2004 datasets, our proposed method shows competitive performance compared to the state-of-the-art methods.

Reliable graph-based collaborative ranking

GRank is a recent graph-based recommendation approach the uses a novel heterogeneous information network to model users’ priorities and analyze it to directly infer a recommendation list. Unfortunately, GRank neglects the semantics behind different types of paths in the network and during the process, it may use unreliable paths that are inconsistent with the general idea of similarity in neighborhood collaborative ranking. That negligence undermines the reliability of the recommendation list generated by GRank.This paper seeks to present a novel framework for reliable graph-based collaborative ranking, called ReGRank, that ranks items based on reliable recommendation paths that are in harmony with the semantics behind different approaches in neighborhood collaborative ranking. To our knowledge, ReGRank is the first unified framework for neighborhood collaborative ranking that in addition to traditional user-based collaborative ranking, can also be adapted for preference-based and representative-based collaborative ranking as well. Experimental results show that ReGRank significantly improves the state-of-the art neighborhood and graph-based collaborative ranking algorithms.

RankUp: Enhancing graph-based keyphrase extraction methods with error-feedback propagation

In recent years, unsupervised, graph-based ranking algorithms have been successfully applied to keyphrase extraction tasks. These methods have the advantage of taking into account global information, such as text structure and relations between words, phrases, and sentences, rather than relying solely on local, vertex-specific information. Graph-based approaches for keyphrase extraction, however, have a particular drawback, which comes from their frequency-based analysis methods. The weakness is that many common, less relevant terms may get a higher ranking, particularly in short articles. The converse situation also occurs, where less common (and possibly more relevant) terms obtain lower rankings. We propose an unsupervised method—RankUp—that enhances graph-based keyphrase extraction approaches by applying an error-feedback mechanism similar to the concept of backpropagation. Experiments have been performed on almost 3,300 short texts from a variety of domains. Our experiments show that error-feedback propagation can boost the quality of keyphrases in graph-based keyphrase extraction techniques.

Moving object detection via robust background modeling with recurring patterns voting

This paper proposes a simple yet effective background modeling method based on recurring patterns voting for moving object detection under challenging scenes. Our method performs the following two steps. First, we employ Gaussian Mixture Model (GMM) to generate the initial background probability map for each frame, in which the value of each pixel represents its probability belonging to the background. Second, we perform recurring patterns voting by employing the graph-based manifold ranking algorithm on the spatially constrained graph to refine the probability map. This prior bases on the observation that the same patterns tend to recur frequently in same semantic regions (background or foreground). We verify it in our problem by calculating the background-background, foreground-foreground and background-foreground densities on some video sequences with ground truths. Experimental results on public video sequences suggest that the proposed method significantly outperforms other moving object detection methods.

Traffic Sign Detection Via Graph-Based Ranking and Segmentation Algorithm

Traffic Sign Detection Via Graph-Based Ranking and Segmentation Algorithm

Salient object detection via compactness and objectness cues

Existing saliency detection algorithms are mainly patch-based. In this paper, we propose a simple but effective approach to detect salient objects by exploring both patch-level and object-level cues. First, we obtain the objectness saliency map with objectness algorithm to find potential object candidates without need of category information. Second, the compactness map is generated by measuring color spatial distribution, and then it is refined by eliminating regions connecting to the selected boundary. Finally, to enforce the consistency among salient regions, we adopt graph-based manifold ranking algorithm by constructing two graphs each using a regional property descriptor. Both qualitative and quantitative evaluations on four publicly available datasets demonstrate the robustness and efficiency of our proposed approach against 23 state-of-the-art methods in terms of six performance criterions.