Abstract

With the advancement of Cloud Computing over the past few years, there has been a massive shift from traditional data centers to cloud enabled data centers. The enterprises with cloud data centers are focusing their attention on energy savings through effective utilization of resources. In this work, we propose algorithms which try to minimize the energy consumption in the data center duly maintaining the SLA guarantees. The algorithms try to utilize least number of physical machines in the data center by dynamically rebalancing the physical machines based on their resource utilization. The algorithms also perform an optimal consolidation of virtual machines on a physical machine, minimizing SLA violations. In extensive simulation, our algorithms achieve savings of about 21% in terms of energy consumption and in terms of maintaining the SLAs, it performs 60% better than Single Threshold algorithm.

Abstract

Sandhi splitting is the primary task for computational processing of text in Sanskrit and Dravidian languages. In these languages, words can join together with morpho-phonemic changes at the point of joining. This phenomenon is known as Sandhi. Sandhi splitter splits the string of conjoined words into individual words. Accurate execution of sandhi splitting is crucial for text processing tasks such as POS tagging, topic modelling and document indexing. We have tried different approaches to address the challenges of sandhi splitting in Malayalam, and finally, we have thought of exploiting the phonological changes that take place in the words while joining. This resulted in a hybrid method which statistically identifies the split points and splits using predefined character level linguistic rules. Currently, our system gives an accuracy of 91.1%.

Abstract

We present here, TagMiner, a data mining approach for part-of-speech (POS) tagging, an important Natural language process-ing (NLP) classification task. It is a semi-supervised associative clas-sification method for POS tagging. Existing methods for building POS taggers require extensive domain and linguistic knowledge and resources.Our method uses combination of a small POS tagged corpus and a raw untagged text data as training data to build the classifier model using association rules. Our tagger works well with very little training dataalso. The use of semi-supervised learning provides the advantage of notrequiring a large high quality tagged corpus. These properties make it es-pecially suitable for resource poor languages. Our experiments on various resource-rich, resource-moderate and resource-poor languages show good performance without using any language specific linguistic information.We note that inclusion of such features in our method may further im-prove the performance. Results also show that for smaller training data sizes our tagger performs better than state-of-the-art CRF tagger using same features as our tagger.

Abstract

Information on web in various languages is growing fast, but large amount of content still exists in English. There are several cases of English text re-use (cross language plagiarism) observed in non-English languages. Detecting text re-use in non-English languages is a challenging task due to complexity of the language used. Complexity further increases for less resource languages like Arabic and Indian languages. In this paper, we address the problem proposed in FIRE CL!TR 2011 task of detecting plagiarized documents in Hindi language which was reused from English language source documents. We proposed three approaches using classification and key-phrase retrieval techniques. Our winning approach attained 0.792 F-measure.

Abstract

This paper presents a novel answer summarization approach for community Question Answering services (cQAs) to address the problem of “incomplete answer”, i.e., missing valuable information from the “best answer” of a complex multi-sentence question, which can be obtained from other answers to the same question. Our method automatically generate a novel and non-redundant summary from cQA answers using structured determinantal point processes (SDPP). Experimental evaluation on sample dataset from Yahoo Answers shows significant improvement over baseline approaches.

Abstract

This paper describes the traditional authorship attribution subtask of the PAN/CLEF 2013 workshop. In our attempt to classify the documents based on gender and age of an author, we have applied a traditional approach of topic modeling using Latent Dirichlet Allocation [LDA]. We used the content based features like topics and style based features like preposition-frequencies, which act as the efficient markers to demarcate the authorship attributes based on age and gender. We demonstrated tenfold cross validation and observed that our classification approach using Maxent and LDA gave an accuracy of 53.3% for English language and 52% for Spanish Language.

Abstract

The ever increasing data on world wide web calls for the use of vertical search engines. Sandhan is one such search engine which offers search in tourism and health genres in more than 10 different Indian languages. In this work we build a URL based genre identification module for Sandhan. A direct impact of this work is on building focused crawlers to gather Indian language content. We conduct experiments on tourism and health web pages in Hindi language. We experiment with three approaches-list based, naive Bayes and incremental naive Bayes. We evaluate our approaches against another web page classification algorithm built on the parsed text of manually labeled web pages. We find that incremental naive Bayes approach outperforms the other two. While doing our experiments we work with different features like words, n-grams and all grams. Using n-gram features we achieve classification accuracies of 0.858 and 0.873 for tourism and health genres respectively.

Abstract

The microblogging platforms are increasingly becoming a lucrative prospect for advertisers to attract the customers. The challenge with advertising on such platforms is that there is very little content to retrieve relevant ads. As the microblogging content is short and noisy and the ads are short too, there is a high amount of lexical/vocabulary mismatch between the micropost and the ads. To bridge this vocabulary mismatch, we propose a conceptual approach that transforms the content into a conceptual space that represent the latent concepts of the content. We empirically show that the conceptual model performs better than various state-of-the-art techniques the performance gain obtained are substantial and significant.

Abstract

In this paper, we propose a novel approach to address the problem of chat summarization. We summarize real-time chat conversations which contain multiple users with frequent shifts in topic. Our approach consists of two phases. In the first phase, we leverage topic modeling using web documents to find the primary topic of discussion in the chat. Then, in the summary generation phase, we build a semantic word space to score sentences based on their association with the primary topic. Experimental results show that our method significantly outperforms the baseline systems on ROUGE F-scores.

Abstract

Content discovery is fast becoming the preferred tool for user engagement on the web. Discovery allows users to get educated and entertained about their topics of interest. StumbleUpon is the largest personalized content discovery engine on the Web, delivering more than 1 billion personalized recommendations per month. As a recommendation system one of the primary metrics we track is whether the user returns (retention) to use the product after their initial experience (session) with StumbleUpon