User-generated reviews are valuable resources for decision making. Identifying the aspect categories discussed in a given review sentence (e.g., “food” and “service” in restaurant reviews) is an important task of sentiment analysis and opinion mining. Given a predefined aspect category set, most previous researches leverage hand-crafted features and a classification algorithm to accomplish the task. The crucial step to achieve better performance is feature engineering which consumes much human effort and may be unstable when the product domain changes. In this paper, we propose a representation learning approach to automatically learn useful features for aspect category detection. Specifically, a semi-supervised word embedding algorithm is first proposed to obtain continuous word representations on a large set of reviews with noisy labels. Afterwards, we propose to generate deeper and hybrid features through neural networks stacked on the word vectors. A logistic regression classifier is finally trained with the hybrid features to predict the aspect category. The experiments are carried out on a benchmark dataset released by SemEval-2014. Our approach achieves the state-of-the-art performance and outperforms the best participating team as well as a few strong baselines.

Collective social and behavioral information commonly exists in nature. There is a widespread intuitive sense that the characteristics of these social and behavioral information are to some extend related to the themes (or semantics) of the activities or targets. In this paper, we explicitly validate the interplay of collective social behavioral information and group themes using a large scale real dataset of online groups, and demonstrate the possibility of perceiving group themes from collective social and behavioral information. We propose a REgularized miXEd Regression (REXER) model based on matrix factorization to infer hierarchical semantics (including both group category and group labels) from collective social and behavioral information of group members. We extensively evaluate the proposed method in a large scale real online group dataset. For the prediction of group themes, the proposed REXER achieves satisfactory performances in various criterions. More specifically, we can predict the category of a group (among 6 categories) purely based on the collective social and behavioral information of the group with the Precision@1 to be 55.16% , without any assistance from group labels or conversation contents. We also show, perhaps counterintuitively, that the collective social and behavioral information is more reliable than the titles and labels of groups for inferring the group categories.

This paper represents a paradigm shift in what advice agents should provide people. Contrary to what was previously thought, we empirically show that agents that dispense optimal advice will not necessary facilitate the best improvement in people's strategies. Instead, we claim that agents should at times suboptimally advise. We provide results demonstrating the effectiveness of a suboptimal advising approach in extensive experiments in two canonical mixed agent-human advice-giving domains. Our proposed guideline for suboptimal advising is to rely on the level of intuitiveness of the optimal advice as a measure for how much the suboptimal advice presented to the user should drift from the optimal value.

Core-selection is a crucial property of social choice functions, or rules, in social choice literature. It is also desirable to address the incentive of agents to cheat by misreporting their preferences. This paper investigates an exchange problem where each agent may have multiple indivisible goods, agents' preferences over sets of goods are assumed to be lexicographic, and side payments are not allowed. We propose an exchange rule called augmented top-trading-cycles (ATTC) procedure based on the original TTC procedure. We first show that the ATTC procedure is core-selecting. We then show that finding a beneficial misreport under the ATTC procedure is NP-hard. Under the ATTC procedure, we finally clarify the relationship between preference misreport and splitting, which is a different type of manipulation.

We propose an indexing scheme for top-k shortest-path distance queries on graphs, which is useful in a wide range of important applications such as network-aware search and link prediction. While considerable effort has been made for efficiently answering standard (top-1) distance queries, none of previous methods can be directly extended for top-k distance queries. We propose a new framework for top-k distance queries based on 2-hop cover and then present an efficient indexing algorithm based on the simple but effective recent notion of pruned landmark labeling. Extensive experimental results on real social and web graphs show the scalability, efficiency and robustness of our method. Moreover, we demonstrate the usefulness of top-k distance queries through an application to link prediction.

A new algorithm via Canonical Correlation Analysis (CCA) is developed in this paper to support more effective cross-modal image clustering for large-scale annotated image collections. It can be treated as a bi-media multimodal mapping problem and modeled as a correlation distribution over multimodal feature representations. It integrates the multimodal feature generation with the Locality Linear Coding (LLC) and co-occurrence association network, multimodal feature fusion with CCA, and accelerated hierarchical k-means clustering, which aims to characterize the correlations between the inter-related visual features in images and semantic features in captions, and measure their association degree more precisely. Very positive results were obtained in our experiments using a large quantity of public data.

Image tweets are becoming a prevalent form of socialmedia, but little is known about their content — textualand visual — and the relationship between the two mediums.Our analysis of image tweets shows that while visualelements certainly play a large role in image-text relationships, other factors such as emotional elements, also factor into the relationship. We develop Visual-Emotional LDA (VELDA), a novel topic model to capturethe image-text correlation from multiple perspectives (namely, visual and emotional). Experiments on real-world image tweets in both Englishand Chinese and other user generated content, show that VELDA significantly outperforms existingmethods on cross-modality image retrieval. Even in other domains where emotion does not factor in imagechoice directly, our VELDA model demonstrates good generalization ability, achieving higher fidelity modeling of such multimedia documents.

We present an intuitive explanation for the limited effectiveness of front-to-end bidirectional heuristic search, supported with extensive evidence from many commonly-studied domains. While previous work has proved the limitations of specific algorithms, we show that any front-to-end bidirectional heuristic search algorithm will likely be dominated by unidirectional heuristic search or bidirectional brute-force search. We also demonstrate a pathological case where bidirectional heuristic search is the dominant algorithm, so a stronger claim cannot be made. Finally, we show that on the four-peg Towers Of Hanoi with arbitrary start and goal states, bidirectional brute-force search outperforms unidirectional heuristic search using pattern-database heuristics.

To judge how much a pair of words (or texts) are semantically related is acognitive process. However, previous algorithms for computing semanticrelatedness are largely based on co-occurrences within textualwindows, and do not actively leverage cognitive human perceptions ofrelatedness. To bridge this perceptional gap, we propose to utilizefree association as signals to capture such human perceptions.However, free association, being manually evaluated,has limited lexical coverage and is inherently sparse. We propose to expand lexical coverage and overcome sparseness by constructing an association network of terms and concepts that combines signals from free association norms and five types of co-occurrences extracted from therich structures of Wikipedia. Our evaluation results validate thatsimple algorithms on this network give competitive results incomputing semantic relatedness between words and between shorttexts.

In emergency medical systems, arriving at the incident locationa few seconds early can save a human life. Thus, this paper is motivated by the need to reduce the response time– time taken to arrive at the incident location after receivingthe emergency call — of Emergency Response Vehicles, ERVs(ex: ambulances, fire rescue vehicles) for as many requests as possible. We expect to achieve this primarily by positioning the ”right” number of ERVs at the ”right” places and at the ”right” times. Given the exponentially large action space(with respect to number of ERVs and their placement) and the stochasticity in location and timing of emergency incidents,this problem is computationally challenging. To that end, ourcontributions building on existing data-driven approaches are three fold:1. Based on real world evaluation metrics, we provide a riskbased optimization criterion to learn from past incident data. Instead of minimizing expected response time, we minimize the largest value of response time such that the risk of finding requests that have a higher value is bounded(ex: Only 10% of requests should have a response time greater than 8 minutes).2. We develop a mixed integer linear optimization formulation to learn and compute an allocation from a set of inputrequests while considering the risk criterion.3. To allow for ”live” reallocation of ambulances, we provide a decomposition method based on Lagrangian Relaxation to significantly reduce the run-time of the optimization formulation.Finally, we provide an exhaustive evaluation on real-world datasets from two asian cities that demonstrates the improvement provided by our approach over current practice and the best known approach from literature.