Abstract

Social media has become highly popular in recent years that people are expressing their views, thoughts about any product, movie through reviews. Reviews are having a great influence on people and decisions made by them. This has led researchers and market analyzers to analyze the opinions of users in reviews and model their preferences. Sometimes reviews are also scored in terms of satisfaction score on any product or movie by customer (ratings). These ratings usually vary on a scale from one to five (stars) or very bad to excellent. In this paper we address the problem of attributing a numerical score (one to five stars) to a review. We view it as a multi-label classification (supervised learning) problem and present two approaches, using Naïve Bayes (NB) and Support Vector Machines (SVM’s). We focus more on feature representations of reviews widely used; problems associated with them and present solutions which address them.

Abstract

Admission Control has been proven essential to avoid overloading of resources and for meeting user service demands in utility driven grid computing. Recent emergence of Cloud based services and the popularity of MapReduce paradigm in Cloud Computing environments make the problem of admission control intriguing. We propose a model that allows one to offer MapReduce jobs in the form of on-demand services. We present a learning based opportunistic algorithm that admits MapReduce jobs only if they are unlikely to cross the overload threshold set by the service provider. The algorithm meets deadlines negotiated by users in more than 80% of cases. We employ an automatically supervised Naive Bayes Classifier to label incoming jobs as admissible and non-admissible. From the list of jobs classified as admissible, we then pick a job that is expected to maximize service provider utility. An external supervision rule automatically evaluates decisions made by the algorithm in retrospect, and trains the classifier. We evaluate our algorithm by simulating a MapReduce cluster hosted in the Cloud that offers a set of MapReduce jobs as services to its users. Our results show that admission control is useful in minimizing losses due to overloading of resources, and by choosing jobs that maximize revenue of the service provider.

Abstract

We present a phrase based query expansion (QE) technique for enterprise search using a domain independent concept thesaurus constructed from Wikipedia link structure. Our approach analyzes article and category link information for deriving sets of related concepts for building up the thesaurus. In addition, we build a vocabulary set containing natural word order and usage which semantically represent concepts. We extract query-representational concepts from vocabulary set with a three layered approach. Concept Thesaurus then yields related concepts for expanding a query. Evaluation on TRECENT 2007 data shows an impressive 9 percent increase in recall for fifty queries. In addition to we also observed that our implementation improves precision at top k results by 0.7, 1, 6 and 9 percent for top 10, top 20, top 50 and top 100 search results respectively, thus demonstrating the promise that Wikipedia based thesaurus holds in domain specific search.

Abstract

The problem of extracting bilingual dictionaries from Wikipedia is well known and well researched. Given the structural and rich multilingual content of Wikipedia, a language independent approach is necessary for extracting dictionaries for various languages more so for under-resourced languages. In our attempt to mine dictionaries for under-resourced languages, we developed an iterative approach to construct parallel corpus for building a dictionary, for which we consider several kinds of Wikipedia article information like title, infobox information, category, article text and dictionaries already built at each phase. The average precision over various datasets is encouraging with maximum precision of 76.7%, performing better than existing systems. As no language-specific resources are used, our method is applicable to any pair of language with special focus on under-resourced languages and hence breaking the language barrier.

Abstract

In order to perform speech summmarization it is necessary to identify important segments in speech signal. The importance of a speech segment can be effectively determined by using infomation from lexical and prosodic features. Standard speech summarization systems depend on ASR transcripts or gold standard human reference summaries to train a supervised system which combines lexical and prosodic features to choose a segment into summary. We propose a method which uses prominence values of syllables in a speech segment to rank the segment for summarization. The proposed method does not depend on ASR transcripts or gold standard human summaries. Evaluation results showed that summaries generated by the proposed method are as good as the summaries generated using tf* idf scores and supervised system trained on gold standard summaries.

Abstract

In this report, we present details about the participation of IIIT Hyderabad in Guided Summarization and Knowledge Base Population tracks at TAC 2010. This year, we enhanced our summaization system with knowledge based measures and utilized domain and sentence tag models to score sentences to suit guided summarization track. We have used an external tool, WikiMiner to identify key concepts in the documents and extract important sentences. We adopted an Information Retrieval based approach for the Entity Linking and Slot Filling tasks. In Entity Linking we identified potential nodes from the Knowledge Base and then identified the mapping node using tf-idf similarity. We achieved very good performance in the Entity Linking task. For Slot Filling task we identified documents from the document collection that might contain attribute information. We extracted the attribute information using a rule based approach.

Abstract

MapReduce has become a popular paradigm for large scale data processing in the cloud. The sheer scale of MapReduce deployments make task assignment in MapReduce an interesting problem. The scale of MapReduce applications presents unique opportunity to use data driven algorithms in resource management. We present a learning based scheduler that uses pattern classification for utilization oriented task assignment in MapReduce. We also present the application of our algorithm to the Hadoop platform. The scheduler assigns tasks by classifying them in two classes, good and bad. From the tasks labeled as good it selects a task that is least likely to overload a worker node. We allow users to plug in their own policy schemes for prioritizing jobs. The scheduler learns the impact of different applications on utilization rather quickly and achieves a user specified level of utilization. Our results show that our scheduler reduces response times of jobs in certain cases by a factor of two.

Abstract

Retrieving answer containing passages is a challenging task in Question Answering. In this paper, we describe a novel passage retrieval methodology using answer type profiles. Our methodology includes two steps: estimation and ranking. In the estimation step, answer type profiles are constructed from question-answer sentence pairs parallel corpus using a statistical alignment model. Each answer type profile consists of triples: the query word, the answering sentence word and the probability of translation. In the ranking step, answer type profiles are incorporated into the Language Modeling framework called Statistical Machine Translation models for Information Retrieval. Using this framework a set of relevant passages are retrieved, given a question. We conducted experiments on FACTOID questions from TREC 2002 to 2006 QA tracks. The experimental results showed significant improvements over different retrieval models including TFIDF, Okapi BM25, Indri and KL-divergence.

Abstract

With the growth of web, people are using it as a medium for expressing their opinions, thoughts through blog posts, reviews (in the form of ratings), and forums. Blogosphere is a place where people read, write their views and make comments on others views or thoughts there by exchanging information. It will be very difficult for any business, organization or individual to go through and understand thoughts expressed by others on a product or topic which they are interested in. Hence a summarization system which extracts, analyze and summarize opinions will be useful. Our Summarization system exactly does the same for blog posts. The entire process of summary generation is done in three stages, extract sentences which are sources for opinion (Opinion Mining), then analyze the extracted opinions to determine polarity (Opinion Analysis) and finally ranking the opinion sentences (Opinion Summarization). We present a classification based approach for extracting and analyzing opinions and how we use this approach to rank sentences for generating summary.

Abstract

This work focuses on attribute extraction from product descriptions. We propose a novel solution to extract attributes of a product from a set of text documents. A graph is constructed from the text using word co-occurrence statistics. We compute word clusters and extract attributes from these clusters using graph based methods. Our solution is able to achieve nearly 80% precision and 45% recall. Experiments show that the methods employed are effective in identifying attributes for different dataset sizes.