Abstract

This paper details the approach to identify Named Entities (NEs) from a large non-English corpus and associate them with appropriate tags, requiring minimal human intervention and no linguistic expertise. The main objective in this paper is to focus on Indian languages like Telugu, Hindi, Tamil, Marathi, etc., which are considered to be resourcepoor languages when compared to English. The inherent structure of Wikipedia was exploited in developing an efficient co-occurrence frequency based NE identification algorithm for Indian Languages. We describe the methods by which English Wikipedia data can be used to bootstrap the identification of NEs in other languages. On a dataset of 2,622 Marathi Wikipedia articles, with around 10,000 NEs manually tagged, an F-Measure of 81.25% was achieved by our system without availing language expertise. Similarly, an F-measure of 80.42% was achieved on around 12,000 NEs tagged within 2,935 Hindi Wikipedia articles.

Abstract

The energy consumption in data center is a key component in sustainable growth of paradigms like cloud computing. As servers are becoming more and more energy efficient, the concerns around network power consumption are increasing. We propose an approach to dynamically decide the number of network devices required to support the current load. In this paper, we evaluate our approach and report findings in terms of energy saving and performance of the network.

Abstract

We present, discuss and evaluate a hybrid approach of live migrating a virtual machine across hosts in a Gigabit LAN. Our hybrid approach takes the best of both the traditional methods of live migration - pre and post-copy. In pre-copy, the cpu state and memory is transferred before spawning the VM on destination host whereas the latter is exactly opposite and spawns the VM on destination right after transferring processor state. In our approach, in addition to processor state, we bundle a lot of useful state information. This includes devices and frequently accessed pages of the VM, aka the working set. This drastically reduces the number of page faults over network while we actively transfer memory. Additionally, on every page fault over the network we transfer a set of pages in its locality in addition to the page itself. We propose a prototype design on KVM/Qemu and present a comparative analysis of pre-copy, post-copy and our hybrid approach.

Abstract

Human moods continuously change over time. Tracking moods can provide important information about psychological and health behavior of an individual. Also, history of mood information can be used to predict the future moods of individuals. In this paper, we try to predict the mood transition of a Twitter user by regression analysis on the tweets posted over twitter time line. Initially, user tweets are automatically labeled with mood labels from time 0 to t-1. It is then used to predict user mood transition information at time t. Experiments show that SVM regression attained less root-mean-square error compared to other regression approaches for mood transition prediction.

Abstract

Identifying and extracting subjective information from News, Blogs and other user generated content has lot of applications. Most of the earlier work concentrated on English data. But, recently subjectivity related research at sentence-level in other languages has increased. In this paper, we achieve sentence-level subjectivity classification using language independent feature weighing and selection methods which are consistent across languages. Experiments performed on 5 different languages including English and South Asian language Hindi show that Entropy based category coverage difference criterion (ECCD) feature selection method with language independent feature weighing methods outperforms other approaches for subjective classification.

Abstract

This paper details the approach to identify Named Entities (NEs) from a large non-English corpus and associate them with appropriate tags, requiring minimal human intervention and no linguistic expertise. The main objective in this paper is to focus on Indian languages like Telugu, Hindi, Tamil, Marathi, etc., which are considered to be resource-poor languages when compared to English. The inherent structure of Wikipedia was exploited in developing an efficient co-occurrence frequency based NE identification algorithm for Indian Languages. We describe the methods by which English Wikipedia data can be used to bootstrap the identification of NEs in other languages which generates a list of NE's. Later, the paper focuses on utilizing this NE list to improve multilingual Entity Filling which showed promising results. On a dataset of 2,622 Marathi Wikipedia articles, with around 10,000 NEs manually tagged, an F-Measure of 81.25% was achieved by our system without availing language expertise. Similarly, an F-measure of 80.42% was achieved on around 12,000 NEs tagged within 2,935 Hindi Wikipedia articles.

Abstract

Focused crawling has wide number of applications in the area of Information Retrieval. It is a crucial part in building domain specific search engines, personalized search tools and extending digital libraries. Be it Google Scholar to search for scholarly articles or Google news to search for news articles, domain specific search is the most widely acclaimed application of focused crawling. Unfortunately, there are very few domain specific search engines available for Indian languages.

Abstract

With the growth of web 2.0, people are using it as a medium to express their opinion and thoughts. With the explosion of blogs, journal like user-generated content on the web, companies, celebrities and politicians are concerned about mining and analyzing the discussions about them or their products. In this paper, we present a method to perform opinion mining and summarize opinions at entity level for English blogs. We first identify various objects (named entities) which are talked about by the blogger, then we identify the modifiers which modify the orientation towards these objects. Finally, we generate object centric opinionated summary from blogs. We perform experiments like named entity identification, entity-modifier relationship extraction and modifier orientation estimation. Experiments and Results presented in this paper are cross verified with the judgment of human annotators.

Abstract

Microblogs such as Facebook, Twitter, Google+ present a nice opportunity for advertising on these blogs that are contextually related to the microblog content. The challenge with advertising on such microblog platforms is that there is very little content to classify the tweet as suitable for advertising, which makes it a very hard classification problem. Despite the importance of this area, little formal published research exists. In this work, we try to address the problem of identifying the microblogs that could be targeted for advertisements. We describe a system that learns to classify which microblogs are suitable for targeted advertising. The experiments are conducted on Twitter data. The system user features derived from the user data, the tweet content and uses external resources such as query logs and term frequency from previous twitter data.

Abstract

Summarization has emerged as an increasingly useful approach to tackle the problem of information overload. Extracting information from online conversations can be of very good commercial and educational value. But majority of this information is present as noisy unstructured text making traditional document summarization techniques difficult to apply. In this paper, we propose a novel approach to address the problem of conversation summarization. We develop an automatic text summarizer which extracts sentences from the conversation to form a summary. Our approach consists of three phases. In the first phase, we prepare the dataset for usage by correcting spellings and segmenting the text. In the second phase, we represent each sentence by a set of predefined features. These features capture the statistical, linguistic and sentimental aspects along with the dialogue structure of the conversation. Finally, in the third phase we use a machine learning algorithm to train the summarizer on the set of feature vectors. Experiments performed on conversations taken from the technical domain show that our system significantly outperforms the baselines on ROUGE F-scores.