In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: Consider Input: Analysing sentiment of tweets is important as it helps to determine the users’ opinion. Knowing people’s opinion is crucial for several purposes starting from gathering knowledge about customer base, e-governance, campaignings and many more. In this report, we aim to develop a system to detect the sentiment from tweets. We employ several linguistic features along with some other external sources of information to detect the sentiment of a tweet. We show that augmenting the 140 character-long tweet with information harvested from external urls shared in the tweet as well as Social Media features enhances the sentiment prediction accuracy significantly.

Output: Sentiment Analysis for Twitter : Going Beyond Tweet Text


Input: Consider Input: We study online boosting, the task of converting any weak online learner into a strong online learner. Based on a novel and natural definition of weak online learnability, we develop two online boosting algorithms. The first algorithm is an online version of boost-by-majority. By proving a matching lower bound, we show that this algorithm is essentially optimal in terms of the number of weak learners and the sample complexity needed to achieve a specified accuracy. This optimal algorithm is not adaptive, however. Using tools from online loss minimization, we derive an adaptive online boosting algorithm that is also parameter-free, but not optimal. Both algorithms work with base learners that can handle example importance weights directly, as well as by rejection sampling examples with probability defined by the booster. Results are complemented with an experimental study.

Output: Optimal and Adaptive Algorithms for Online Boosting


Input: Consider Input: This paper presents a theoretical analysis of multi-view embedding – feature embedding that can be learned from unlabeled data through the task of predicting one view from another. We prove its usefulness in supervised learning under certain conditions. The result explains the effectiveness of some existing methods such as word embedding. Based on this theory, we propose a new semi-supervised learning framework that learns a multi-view embedding of small text regions with convolutional neural networks. The method derived from this framework outperforms state-of-the-art methods on sentiment classification and topic categorization.


Output: Semi-Supervised Learning with Multi-View Embedding: Theory and Application with Convolutional Neural Networks