Abstract

An ontology is defined using concepts and relationships between the concepts. In this paper, we focus on second problem: relation extraction from plain text. Generic Knowledge Bases like YAGO, Freebase, and DBPedia have made accessible huge collections of facts and their properties from various domains. But acquiring and maintaining various facts and their relations from domain specific corpus becomes very important and challenging task due to low availability of annotated data. Here, we proposed a label propagation based semi-supervised approach for relation extraction by choosing most informative instances for annotation. We also proposed weakly supervised approach for data annotation using generic ontologies like Freebase, which further reduces the cost of annotating data manually. We checked efficiency of our approach by performing experiments on various domain specific corpora.

Abstract

Resource management in modern data centers has become a challenging task due to the tremendous growth of data centers. In large virtual data centers, performance of applications is highly dependent on the communication bandwidth available among virtual machines. Traditional algorithms either do not consider network I/O details of the applications or are computationally intensive. We address the problem of identifying the virtual machine clusters based on the network traffic and placing them intelligently in order to improve the application performance and optimize the network usage in large data center. We propose a greedy consolidation algorithm that ensures the number of migrations is small and the placement decisions are fast, which makes it practical for large data centers. We evaluated our approach on real world traces from private and academic data centers, using simulation and compared the existing algorithms on various parameters like scheduling time, performance improvement and number of migrations. We observed a ~70% savings of the interconnect bandwidth and overall ~60% improvements in the applications performances. Also, these improvements were produced within a fraction of scheduling time and number of migrations.

Abstract

Wikipedia being a large, freely available, frequently updated and community maintained knowledge base, has been central to much recent research. However, quite often we find that the information extracted from it has extraneous content. This paper proposes a method to extract useful information from Wikipedia, using Semantic Features derived from Wikipedia categories. The proposed method provides good performance as a Wikipedia category based method. Experimental results on benchmark datasets show that the proposed method achieves a correlation coefficient of 0.66 with human judgments. The Semantic Features derived by this method gave good correlation with human rankings in a web search query completion application.

Abstract

Knowledge bases provide with the benefit of organizing knowledge in the relational form but suffer from incompleteness of new entities and relationships. Prior work on relation extraction has been focused on supervised learning techniques which are quite expensive. An alternative approach based on distant supervision has been of significant interest where one aligns database records with sentences of these records. A new line of work on embeddings of symbolic representations [2] has shown promise. We introduce a Matrix trifactorization model which can find missing information in knowledge bases. Experiments show that we are able to query and find missing information from text and shows improvement over existing methods.

Abstract

The availability of increasingly richer applications is providing surprisingly wide range of functionalities and new use cases on mobile devices. Even tough mobile devices are becoming increasingly more powerful, the resource utilization of richer application can overwhelm resources on these devices. At the same time, ubiquitous connectivity of mobile devices also opens up the possibility of leveraging cloud resources. Seamless and flexible path to mobile cloud computing requires recognizing opportunities where the application execution on cloud instead of mobile device. In this paper we propose a cloud aware scheduler for application offloading from mobile devices to clouds. We used learning based algorithm for predicting the gain attainable using performance monitoring and high level features. We evaluated prototype of our system on various workloads and under various conditions.

Abstract

Building bilingual dictionaries from Wikipedia has been extensively studied in the area of computation linguistics. These dictionaries play a crucial role in Natural Language Processing(NLP) applications like Cross-Lingual Information Retrieval, Machine Translation and Named Entity Recognition. To build these dictionaries, most of the existing approaches use information present in Wikipedia titles, info-boxes and categories. Interestingly, not many use the structural properties of a document like sections, subsections, etc. In this work we exploit the structural properties of documents to build a bilingual English-Hindi dictionary. The main intuition behind this approach is that documents in different languages discussing the same topic are likely to have similar structural elements. Though we present our experiments only for Hindi, our approach is language independent and can be easily extended to other languages. The major contribution of our work is that the dictionary contains translation and transliteration of words which include Named Entities to a large extent. We evaluate our dictionary using manually computed precision. We generated a massive list of 72k tokens using our approach with 0.75 precision.

Abstract

Building bilingual dictionaries from Wikipedia has been extensively studied in the area of computation linguistics. These dictionaries play a crucial role in Natural Language Processing (NLP) applications like Cross-Lingual Information Retrieval, Machine Translation and Named Entity Recognition. To build these dictionaries, most of the existing approaches use information present in Wikipedia titles, info-boxes and categories. Interestingly, not many use the structural properties of a document like sections, subsections, etc. In this work we exploit the structural properties of documents to build a bilingual English-Hindi dictionary. The main intuition behind this approach is that documents in different languages discussing the same topic are likely to have similar structural elements. Though we present our experiments only for Hindi, our approach is language independent and can be easily extended to other languages. The major contribution of our work is that the dictionary contains translation and transliteration of words which include Named Entities to a large extent. We evaluate our dictionary using manually computed precision. We generated a massive list of 72k tokens using our approach with 0.75 precision.

Abstract

In this paper, we describe our system for the SemEval-2013 Task 2, Sentiment Analysis in Twitter. We formed features that take into account the context of the expression and take a supervised approach towards subjectivity and polarity classification. Experiments were performed on the features to find out whether they were more suited for subjectivity or polarity Classification. We tested our model for sentiment polarity classification on Twitter as well as SMS chat expressions, analyzed their F-measure scores and drew some interesting conclusions from them. Total citations Cited by 3

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

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.