We show recall of the learned Chinese sentiment words in Table 3. Compared with BLP and SOP, the Rule approach learns fewer sentiment words. The coverage of the Rule approach is inevitably low because many words in the corpus are aligned to both positive and negative words. For example, in most cases the positive Chinese word (helpful) is aligned to the positive English word helpful. But sometimes it is aligned (or misaligned) to the negative English words, like freak. Under this situation, the word tends to be predicted as objective. In SOP, the positive and negative scores are related to the distances of the word to the positive and negative seed words, and the distance is usually coarse-grained to depict the sentiment polarity. For example, the shortest path between the word good and the word bad in WordNet is only 5 (Kamps et al. 2004). The Rule and SOP approaches find different sentiment words. We then evaluate the learned Chinese polarity word lists by precision at k. As illustrated in Figure 4, the significance test indicates that our approach significantly outperforms the Rule and SOP approaches. The major difference of our approach is that the polarity information can be transferred between English and Chinese and within each language at the same time, whereas in the other two approaches the polarity information mainly transfers from English to Chinese and once a word gets a polarity score, it is difficult to change or refine. The idea of the MAD approach is similar to bilingual graph label propagation, but the MAD
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Knowledge graph will be usefull for the intelligent system. As the relationship prediction on the knowledge graph becomes accurate, construction of a knowledge graph and detection of erroneous information included in a knowledge graph can be performed more conveniently. The goal of our research is to predict a relation (predicate) of two given Knowledge Graph (KG) entities (subject and object). Link prediction between entities is important for developing large-scale ontologies and for KG completion. TransE and TransR have been proposed as the methods for such a prediction. However, TransE and TransR embed both entities and relations in the same (or different) semantic space(s). In this research we propose a simple architecture model with emphasis on relation prediction by using a Multi-Label Deep Neural Network (DNN), and developed KGML. KGML embeds entities only; given subject and object are embedded and concatenated to predict probability distribution of
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1] : Xi Li at. Al. Joint Multilabel Classification With Community-Aware Label Graph Learning in IEEE 2016. propose a multi label classification framework based on a joint learning method called label graph learning (LGL) driven weighted Support Vector Machine (SVM). In belief, the joint learning method explicitly models the inter-label correlations by LGL, which is jointly optimized with multi label cataloging in a unified learning scheme. As aoutcome, the learned label correlation graph well fits the multilabel classification task though efficiently shimmering the original topological structures amongst labels. Furthermore, the inter- label connections are also inclined by label-specific sample communities (each community for the samples distribution a common label). Namely, if two labels have parallel label- specific sample communities, they are likely to be correlated. Based on this observation, LGL is further regularized by the label HypergraphLaplacian.
We present three distinct contributions. First we consider biomedical segmentation where the object of interest comprises multiple distinct regions of uncertain shape (e.g. blood vessels, airways, bone tissue). We show that this common yet difficult scenario can be modeled as an energy over multiple interacting surfaces, and can be globally optimized by a single graph cut. Second, we introduce multi-label energies with label costs and provide algorithms to minimize them. We show how label costs are useful for clustering and robust estimation problems in vision. Third, we characterize a class of energies with hierarchical costs and propose a novel hierarchical fusion algorithm with improved approximation guarantees. Hierarchical costs are natural for modeling an array of difficult problems, e.g. segmentation with hierarchical context, simultaneous estimation of motions and homographies, or detecting hierarchies of patterns.
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In this paper, we proposed a graph-based frame- work for TC. By treating the term weighting task as a node ranking problem of interconnected fea- tures defined by a graph, we were able to deter- mine the importance of a term using node central- ity criteria. Building on this formulation, we intro- duced simple-yet-effective weighting schemes at the collection and label level, in order to penalize globally important terms (as analogous to “glob- ally frequent terms”) and reward locally impor- tant terms respectively. We also incorporate ad- ditional word-embedding information as weights in the graph-based representations.
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There is high demand for automated tools that assign polarity to microblog content such as tweets (Twitter posts), but this is challenging due to the terseness and informality of tweets in addition to the wide variety and rapid evolu- tion of language in Twitter. It is thus impracti- cal to use standard supervised machine learn- ing techniques dependent on annotated train- ing examples. We do without such annota- tions by using label propagation to incorpo- rate labels from a maximum entropy classifier trained on noisy labels and knowledge about word types encoded in a lexicon, in combina- tion with the Twitter follower graph. Results on polarity classification for several datasets show that our label propagation approach ri- vals a model supervised with in-domain an- notated tweets, and it outperforms the nois- ily supervised classifier it exploits as well as a lexicon-based polarity ratio classifier.
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Case-2:Let T V : . Then, with respect to the function , label the vertices of by distinct singleton sets and distinct non-singleton sets in such a way that one edge in the path common to all cycles is mono-indexed. Now, all the odd cycles have a mono-indexed edge. But, since even cycles have even number of mono-indexed edges, every even cycle contains another mono-indexed edge under . Therefore, has at least T 1 mono-
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In the proposed method and baselines, we use skip gram (Mikolov et al., 2013) to get pre-trained word embeddings of reviews. The Jieba toolkit is used to split sentences into words. To help with the training of the label graph, we use soft tar- get mechanism on the continuous label represen- tations (Hinton et al., 2015; Sun et al., 2017; Liu et al., 2017) and add the negative of the l2 loss of the difference between the label graph and the identity matrix to the loss function. For evaluation, we introduce a hyper-parameter p. If the proba- bility of a style is greater than p, we consider it as one of the final music styles. We tune hyper- parameters based on the performance on the val- idation set. We set p to 0.2, hidden size to 128, embedding size to 128, vocabulary size to 135K, learning rate to 0.001, and batch size to 128. The optimizer is Adam (Kingma and Ba, 2014) and the maximum training epoch is set to 100. We choose parameters with the best Micro F1 scores on the validation set and then use the selected parameters on the test set.
Current approaches have high performance — a system will recall around 81% of the arguments cor- rectly and 95% of those will be assigned a cor- rect semantic role (see M`arquez et al. (2008) for details), however only on languages and domains for which large amounts of role-annotated training data are available. For instance, systems trained on PropBank demonstrate a marked decrease in per- formance (approximately by 10%) when tested on out-of-domain data (Pradhan et al., 2008). Unfortu- nately, the reliance on role-annotated data which is expensive and time-consuming to produce for every language and domain, presents a major bottleneck to the widespread application of semantic role labeling. In this paper we argue that unsupervised meth- ods offer a promising yet challenging alternative. If successful, such methods could lead to significant savings in terms of annotation effort and ultimately yield more portable semantic role labelers that re- quire overall less engineering effort. Our approach formalizes semantic role induction as a graph parti- tioning problem. Given a verbal predicate, it con- structs a weighted graph whose vertices correspond to argument instances of the verb and whose edge weights quantify the similarity between these in- stances. The graph is partitioned into vertex clus- ters representing semantic roles using a variant of Chinese Whispers, a graph-clustering algorithm pro- posed by Biemann (2006). The algorithm iteratively assigns cluster labels to graph vertices by greedily choosing the most common label amongst the neigh- bors of the vertex being updated. Beyond extend- ing Chinese Whispers to the semantic role induc- tion task, we also show how it can be understood as a type of Gibbs sampling when our graph is inter- preted as a Markov random field.
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(valuation) of a graph is an assignment of labels from a set of positive integers to the vertices of that induce a label for each edge defined by the labels and . If is any simple graph with edges, then an injective function is said to be graceful, when each edge is assigned the label , the resulting edge labels are distinct. In 2012, Sridevi, Navaneethakrishnan, A. Nagarajan and K. Nagarajan  defined a graph is odd-even graceful if there is an injection from to such that when each edge is assigned the label , the resulting edge labels are They have verified the odd even gracefulness of some known standard graphs. In 1977, Bodendiek conjectured that any cycle with a chord is graceful and later it is verified by Delorme in 1984. In analogous to this the graph, cycle with parallel chords has been defined and many authors, ,  have verified its gracefulness. In 1991, Gnanajothi defined a graph to have odd graceful labeling if there is an injection from to such that when each edge is assigned the label , the resulting edge labels are For detailed survey refer to the dynamic survey by Gallian.
in Section ‘Display graphs and edge label intersection graphs’) leads to a new, and natural, characterization of compatibility in terms of minimal cuts in the display graph (Section ‘Characterizing compatibility via cuts’). We then show how such cuts are closely related to the splits of the compatible supertree (Section ‘Splits and cuts’). Next, we give a characterization of the agreement in terms of minimal cuts of the display graph (Section ‘Characterizing agreement via cuts’). To our knowledge, there was no pre- vious characterization of the agreement supertree prob- lem for unrooted trees. Lastly, we examine the connection between the triangulation-based and the cut-based per- spectives on compatibility (Section ‘Relationship to legal triangulations’).
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We also measured the variation of performance with context size, and evaluated the disambiguation ac- curacy for both algorithms for a window size rang- ing from two words to an entire sentence. The win- dow size parameter limits the number of surround- ing words considered when seeking label dependen- cies (sequence data labeling), or the words counted in the measure of definition–context overlap (individ- ual data labeling). Figure 3 plots the disambiguation accuracy of the two algorithms as a function of con- text size. As seen in the figure, both algorithms ben- efit from larger contexts, with a steady increase in performance observed for increasingly larger window sizes. Although the initial growth observed for the se- quence data labeling algorithm is somewhat sharper, the gap between the two curves stabilizes for window sizes larger than five words, which suggests that the improvement in performance achieved with sequence data labeling over individual data labeling does not de- pend on the size of available context.
We describe a novel approach for inducing unsupervised part-of-speech taggers for lan- guages that have no labeled training data, but have translated text in a resource-rich lan- guage. Our method does not assume any knowledge about the target language (in par- ticular no tagging dictionary is assumed), making it applicable to a wide array of resource-poor languages. We use graph-based label propagation for cross-lingual knowl- edge transfer and use the projected labels as features in an unsupervised model (Berg- Kirkpatrick et al., 2010). Across eight Eu- ropean languages, our approach results in an average absolute improvement of 10.4% over a state-of-the-art baseline, and 16.7% over vanilla hidden Markov models induced with the Expectation Maximization algorithm.
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In future work, we would like to include transla- tions for infrequent phrases which are not OOVs. We would like to explore new propagation meth- ods that can directly use confidence estimates and control propagation based on label sparsity. We also would like to expand this work for mor- phologically rich languages by exploiting other resources like morphological analyzer and cam- pare our approach to the current state of art ap- proaches which are using these types of resources. In conclusion, we have shown significant improve- ments to the quality of statistical machine transla- tion in three different cases: low resource SMT, domain shift, and morphologically complex lan- guages. Through the use of semi-supervised graph propagation, a large scale multilingual paraphrase database can be used to improve the quality of sta- tistical machine translation.
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A statistical analysis of the segmentation and tag- ging results of the supervised joint model (Base- line II) and our model is carried out to comprehend the influence of the graph-based semi-supervised behavior. For word segmentation, the most signif- icant improvement of our model is mainly concen- trated on two kinds of words which are known for their difficulties in terms of CWS: a) named enti- ties (NE), e.g., “ 天 津港” (Tianjin port) and “保 税 区” (free tax zone); and b) Chinese numbers (CN), e.g., “ 八 点五亿” (eight hundred and fifty million) and “百分之七十二” (seventy two percent). Very often, these words do not exist in the labeled data, so the supervised model is hard to learn their fea- tures. Part of these words, however, may occur in the unlabeled data. The proposed semi-supervised approach is able to discover their label information with the help of a similarity graph. Specifically, it learns the label distributions from similar words (neighborhoods), e.g., “上海港” (Shanghai port), “保 护 区” (protection zone), “九 点 七亿” (nine hundred and seventy million). The statistics in Ta- ble 5 demonstrate significant error reductions of 50.44% and 48.74% on test data, corresponding to NE and CN respectively.
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In single label image classification, the input images will consist of 10 classes each class contains 100 images. Hence total 1000 images are used for single label classification. The input images will have single instances. Identify visual features of images and their labels of each image. For multi-label image classification we use NUSWIDE dataset. Using this data set correlated labels are classified. For the testing phase, the input images will be given by users. Discover the visual features of testing image and find the labels. Find correlation between labels and more valid and accurate labels are taken out. For this implementation we are using Java platform on windows operating system. Precision is the fraction of true positive to true positive with false negative. Performance of the system will be measured by calculating the precision metrics. Precision for the image retrieval is shown in below table 2. Precision graph is shown in figure 2. Precision is calculated for 5 images from each class.
Fine-Grained Entity Typing The task of fine- grained entity typing was first thoroughly inves- tigated in (Ling and Weld, 2012), which utilized Freebase-guided distant supervision (DS) (Mintz et al., 2009) for entity typing and created one of the early large-scale datasets. Although DS pro- vides an efficient way to annotate training data, later work (Gillick et al., 2014) pointed out that en- tity type labels induced by DS ignore entities’ lo- cal context and may have limited usage in context- aware applications. Most of the following research has since focused on testing in context-dependent scenarios. While early methods (Gillick et al., 2014; Yogatama et al., 2015) on this task rely on well-designed loss functions and a suite of hand- craft features that represent both context and enti- ties, Shimaoka et al. (2016) proposed the first at- tentive neural model which outperformed feature- based methods with a simple cross-entropy loss. Modeling Entity Type Correlations To bet- ter capture the underlying label correlations, Shi- maoka et al. (2017) employed a hierarchical label encoding method and AFET (Ren et al., 2016a) used the predefined label hierarchy to identify noisy annotations and proposed a partial-label loss to reduce such noise. A recent work (Xu and Barbosa, 2018) proposed hierarchical loss nor- malization which alleviated the noise of too spe- cific types. Our work differs from these works in that we do not rely on known label structures and aim to learn the underlying correlations from data. Rabinovich and Klein (2017) recently pro- posed a structure-prediction approach which used type correlation features. The inference on their learned factor graph is approximated by a greedy decoding algorithm, which outperformed unstruc- tured methods on their own dataset. Instead of us- ing an explicit graphical model, we enforce a re- lational bias on model parameters, which does not introduce extra burden on label decoding.
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In graph-based semi-supervised learning, one con- structs a graph whose vertices are labeled and unla- beled examples. Weighted edges in the graph, con- necting pairs of examples/vertices, encode the de- gree to which they are expected to have the same label (Zhu et al., 2003). Variants of label propaga- tion are used to transfer labels from the labeled to the unlabeled examples. There are several instances of the use of graph-based methods for natural language tasks. Most relevant to our work an approach to word-sense disambiguation due to Niu et al. (2005). Their formulation was transductive, so that the test data was part of the constructed graph, and they did not consider predicate-argument analysis. In con- trast, we make use of the smoothed graph during in- ference in a probabilistic setting, in turn using it for the full frame-semantic parsing task. Recently, Sub- ramanya et al. (2010) proposed the use of a graph over substructures of an underlying sequence model, and used a smoothed graph for domain adaptation of part-of-speech taggers. Subramanya et al.’s model was extended by Das and Petrov (2011) to induce part-of-speech dictionaries for unsupervised learn- ing of taggers. Our semi-supervised learning setting is similar to these two lines of work and, like them, we use the graph to arrive at better final structures, in an inductive setting (i.e., where a parametric model is learned and then separately applied to test data, following most NLP research).
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Smart phone Apps plays a vital role to attract mobile-Advertising. Popular apps can generate millions of dollars in profit and collect valuable personal user information. Spam, i.e., fraudulent or invalid tap or click on online ads, where the user has no actual interest in the advertiser’s site, results in advertising revenue being misappropriated by spammers. It requires a user touch or click on control ads came from Smartphone-game Apps. It all need the user to tap the screen close to where the ad is displayed .While ad networks take active measures to block click-spam today, but not in mobile advertising. The presence of spam in mobile advertising is largely unknown. In this paper, we take the first systematic look at spam in mobile advertising. Then we design a Graph based label propagation algorithm on click-through data to identify spam Apps in Smartphone- game Apps. We validate our methodology using data from major ad networks. Our findings highlight the severity of the spam in mobile advertising.
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We consider simple, finite, undirected graph G = (V, E). R. Ponraj, M. Maria Adaickalam and R. Kala  introduced k- difference cordial labeling of graphs. In , they investigated k-difference cordial labeling behavior of star, m copies of star and proved that every graph is a subgraph of a connected k-difference cordial graph. In , R. Ponraj and M. Maria Adaickalam discussed the 3-difference cordial label- ing behavior of path, cycle, star, bistar, complete graph, complete bipartite graph, comb, double comb, quadrilateral snake. For the standard terminology and notations we follow Harary.