due to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. this problem deals with information extraction and knowledge discovery from textual data using natural language processing has attracted the attention of many researchers. construction of sentiment lexicon as a valuable language resource is a one of the imp...

Due to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. This problem deals with information extraction and knowledge discovery from textual data using natural language processing has attracted the attention of many researchers. Construction of sentiment lexicon as a valuable language resource is a one of the imp...

In this work a novel approach for weakly supervised object detection that incorporates pointwise mutual information is presented. A fully convolutional neural network architecture is applied in which the network learns one filter per object class. The resulting feature map indicates the location of objects in an image, yielding an intuitive representation of a class activation map. While tradit...

In this paper, we tackle the problem of temporally consistent boundary detection and hierarchical segmentation in videos. While finding the best high-level reasoning of region assignments in videos is the focus of much recent research, temporal consistency in boundary detection has so far only rarely been tackled. We argue that temporally consistent boundaries are a key component to temporally ...

The paper describes our application of the distributional semantic model (DSM) method that we developed for The First International Workshop on Russian Semantic Similarity Evaluation (RUSSE) shared relatedness task. The model was trained, for the most part, on the data of the Russian National Corpus main subcorpus (around 200 mln tokens), and the resulting vector space was weighted according to...

Methods for dimensionality reduction, notably LSA, have been successfully applied to the information retrieval task and document classification. Recently, corpus-based association measures such as point-wise mutual information have been found to outperform LSA on a variety of tasks. We have developed an algorithmic framework that computes a low-dimensional vector space representation of documen...

In this paper, we propose LexVec, a new method for generating distributed word representations that uses low-rank, weighted factorization of the Positive Point-wise Mutual Information matrix via stochastic gradient descent, employing a weighting scheme that assigns heavier penalties for errors on frequent cooccurrences while still accounting for negative co-occurrence. Evaluation on word simila...

This paper presents a novel method for acquiring a set of query patterns to retrieve documents containing important information about an entity. Given an existing Wikipedia category that contains the target entity, we extract and select a small set of query patterns by presuming that formulating search queries with these patterns optimizes the overall precision and coverage of the returned Web ...

We have developed a new mutual information-based registration method for matching unlabeled point features. In contrast to earlier mutual information-based registration methods, which estimate the mutual information using image intensity information, our approach uses the point feature location information. A novel aspect of our approach is the emergence of correspondence (between the two sets ...