Abstract

Following the footsteps of SemEval-2014 Task 4 (Pontiki et al., 2014), SemEval-2015 too had a task dedicated to aspect-level sentiment analysis (Pontiki et al., 2015), which saw participation from over 25 teams. In Aspectbased Sentiment Analysis, the aim is to identify the aspects of entities and the sentiment expressed for each aspect. In this paper, we present a detailed description of our system, that stood 4th in Aspect Category subtask (slot 1), 7th in Opinion Target Expression subtask (slot 2) and 8th in Sentiment Polarity subtask (slot 3) on the Restaurant datasets.

Abstract

On various microblogging platforms like Twitter, the users post short text messages ranging from news and information to thoughts and daily chatter. These messages often contain keywords called Hashtags, which are semantico-syntactic constructs that enable topical classification of the microblog posts. In this poster, we propose and evaluate a novel method of semantic enrichment of microblogs for a particular type of entity search--retrieving a ranked list of the top-k hashtags relevant to a user's query Q. Such a list can help the users track posts of their general interest. We show that our technique significantly improved microblog retrieval as well. We tested our approach on the publicly available Stanford sentiment analysis tweet corpus. We observed an improvement of more than 10% in NDCG for microblog retrieval task, and around 11% in mean average precision for hashtag retrieval task.

Abstract

This paper describes the system that was submitted to SemEval2015 Task 10: Sentiment Analysis in Twitter. We participated in Subtask B: Message Polarity Classification. The task is a message level classification of tweets into positive, negative and neutral sentiments. Our model is primarily a supervised one which consists of well designed features fed into an SVM classifier. In previous runs of this task, it was found that lexicons played an important role in determining the sentiment of a tweet. We use existing lexicons to extract lexicon specific features. The lexicon based features are further augmented by tweet specific features. We also improve our system by using acronym and emoticon dictionaries. The proposed system achieves an F1 score of 59.83 and 67.04 on the Test Data and Progress Data respectively. This placed us at the 18th position for the Test Dataset and the 16th position for the Progress Test Dataset.

Abstract

Following the footsteps of SemEval-2014 Task 4 (Pontiki et al., 2014), SemEval-2015 too had a task dedicated to aspect-level sentiment analysis (Pontiki et al., 2015), which saw participation from over 25 teams. In Aspectbased Sentiment Analysis, the aim is to identify the aspects of entities and the sentiment expressed for each aspect. In this paper, we present a detailed description of our system, that stood 4th in Aspect Category subtask (slot 1), 7th in Opinion Target Expression subtask (slot 2) and 8th in Sentiment Polarity subtask (slot 3) on the Restaurant datasets.

Abstract

Like SemEval 2013 and 2014, the task Sentiment Analysis in Twitter found a place in this year’s SemEval too and attracted an unprecedented number of participations. This task comprises of four sub-tasks. We participated in subtask 2—Message polarity classification. Although we lie a few notches down from the top system, we present a very simple yet effective approach to handle this problem that can be implemented in a single day!

Abstract

Universities across the world are increasingly trying to become more entrepreneurial, in order to stay competitive, generate new sources of income through licensing or contract research, and follow policy guidelines from governments. The most powerful resource universities have to stimulate entrepreneurship is their students. However, there is no evaluated theory on how to encourage students to become entrepreneurs. Through three case studies the entrepreneurial encouragement offerings applied at MIT in the United States, IIIT in India, and Utrecht University in the Netherlands are investigated. The offerings provided by these institutes have been surveyed, interviews about these offerings with university staff have been performed, and reflected upon through interviews with entrepreneurs that graduated from these institutes. The three case studies provide insight in how student entrepreneurship encouragement offerings contributed to students choosing a career as an entrepreneur. Several successful examples of student entrepreneurship encouragement offerings are presented, and a model is proposed on how to effectively encourage entrepreneurship among students. The model supports academic institutes in constructing an environment that encourages student entrepreneurship and aims to help universities convince students to continue their careers as entrepreneurs.

Abstract

Various projections or views of a heterogeneous information network can be modeled using the graph OLAP (On-line Analytical Processing) framework for effective decision making. Detecting anomalous projections of the network can help the analysts identify regions of interest from the graph specific to the projection attribute. While most previous studies on outlier detection in graphs deal with outlier nodes, edges or subgraphs, we are the first to propose detection of graph cuboid outliers. Further we perform this detection in a query sensitive way. Given a general subgraph query on a heterogeneous network, we study the problem of finding outlier cuboids from the graph OLAP lattice. A Graph Cuboid Outlier (GCOutlier) is a cuboid with exceptionally high density of matches for the query. The GCOutlier detection task is clearly challenging because: (1) finding matches for the query (subgraph isomorphism) is NP-hard; (2) number of matches for the query can be very high; and (3) number of cuboids can be large. We provide an approximate solution to the problem by computing only a fraction of the total matches originating from a select set of candidate nodes and including a select set of edges, chosen smartly. We perform extensive experiments on synthetic datasets to showcase the execution time versus accuracy trade-off. Experiments on real datasets like Four Area and Delicious containing thousands of nodes reveal interesting GCOutliers.

Abstract

on the path are shown in Figure 6;(a) O1 from a;(b) O1 from b;(c) O1 from c;(d) O1 from d; and (e) O2 from e.(f) O2 from f.

Abstract

Localization in 3D is an important problem with wide ranging applications from autonomous navigation in robotics to location specific services on mobile devices. GPS sensors are a commercially viable option for localization, and are ubiquitous in their use, especially in portable de-vices. With the proliferation of mobile cameras however, maturing localization algorithms based on computer vision are emerging as a viable alternative. Although both vision and GPS based localization algorithms have many limita-tions and inaccuracies, there are some interesting compli-mentarities in their success/failure scenarios that justify an investigation into their joint utilization. Such investigations are further justified considering that many of the modern wearable and mobile computing devices come with sensors for both GPS and vision. In this work, we investigate approaches to reinforce GPS localization with vision algorithms and vice versa. Specif-ically, we show how noisy GPS signals can be rectified by vision based localization of images captured in the vicin-ity. Alternatively, we also show how GPS readouts might be used to disambiguate images when they are visually similar looking but belong to different places. Finally, we empiri-cally validate our solutions to show that fusing both these approaches can result in a more accurate and reliable lo-calization of videos captured with a Contour action camera, over a 600 meter long path, over 10 different days.

Abstract

In this work, we address the problem of cross-modal retrieval in presence of multi-label annotations. In particular, we introduce multi-label Canonical Correlation Analysis (ml-CCA), an extension of CCA, for learning shared subspaces taking into account high level semantic information in the form of multi-label annotations. Unlike CCA, ml-CCA does not rely on explicit pairing between modalities, instead it uses the multi-label information to establish correspondences. This results in a discriminative subspace which is better suited for cross-modal retrieval tasks. We also present Fast ml-CCA, a computationally efficient version of ml-CCA, which is able to handle large scale datasets. We show the efficacy of our approach by conducting extensive cross-modal retrieval experiments on three standard benchmark datasets. The results show that the proposed approach achieves state of the art retrieval performance on the three datasets.