Abstract

Given a knowledge base, annotating any text with entities in the knowledge base enhances automated understanding of the text. Entities provide extra contextual information for the automated system to understand and interpret the text better. In the special case when the text is in the form of short text queries, automated understanding can be critical in improving the quality of search results and recommendations. Annotation of queries helps semantic retrieval, ensuring diversity of search results including retrieval of relevant news stories. In this paper, we present SIEL@ERD, a system for automated stamping of entity information in short query text. Our system builds from the state-of-the-art TAGME system and is optimized for time and performance efficiency. Our system achieved an F1 measure of 0.53 and the latency of 0.31 seconds on a dataset of 500 queries and a Freebase snapshot provided for the short track in the Entity Recognition and Disambiguation Challenge at SIGIR 2014.

Abstract

Our computing demands have grown so much that we need a robust distributed computing platform to process data. To feed such data hungry systems, we need an equally robust distributed file systems that span across multiple geographically separate locations. Most distributed file systems break any file into a set of blocks or chunks which are spread across the cluster. The major bottleneck is to identify where to place the blocks and where to place the replicas so that cluster is optimized on certain parameters like disk utilization, minimizing network congestion, maximizing throughput, optimal power utilization, etc. This paper proposes assigning a distance measure for each one of the data sources with respect to the other and placing the blocks on the specified disk on the node that minimizes the total distance of the last few requests made for that block. As the request pattern and parameters change, the distances are updated and the blocks are moved dynamically to minimized the distance, in effect optimizing on the required parameters. The distance function is to be modeled based on the cluster and the parameters you wish to optimize on. It can be a function of just bandwidth or bandwidth and latency or other miscellaneous features like disk utilization, processing power, disk speed, power utilization, cooling requirements, temperature, etc. A detailed performance analysis was carried out with disk bandwidth, network bandwidth and disk utilization as the parameters and the performance is far better in comparison to the reference system (10% or more depending on specification differences) which has no understanding of the different type of disks present and the nature of the cluster. Due to the performance aware nature, the system was inherently able to utilize memory for speeding up performance through the use of in-memory partitions.

Abstract

Current PaaS solutions use an underlying IaaS provider and restrict themselves to a few geographical regions. They operate in one/two zones only and use others for failover or disaster recovery. In this paper, we present the design of MultiPaaS, a PaaS solution which runs on multiple cloud/IaaS providers and leverages the combined global span. Unlike traditional scaling techniques where app servers are scaled up or down in a specific region or data center, we present a design to perform it on a global scale. Origin of the request burst is identified and instances are booted in a data center closest to it. It ensures database persistence and cross-region scalability via a combination of replication techniques. We identify common features available across all IaaS providers and use that as a provider interface to ensure interoperability, but use provider specific extensions to leverage unique offerings and benefits.

Abstract

Computational Advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the Web. The context depends on the type of advertising and could mean — content where the ad is shown, the user who is viewing the ad or the social network of the user. Computational Advertising (CA) is a scientific sub-discipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search and text analysis. The core problem addressed in Computational Advertising is of match-making between the ads and the context.CA is prevalent in three major forms on the Web. One of the forms involves showing textual ads relevant to a query on the search page, known as Sponsored Search. On the other hand, showing textual ads relevant to a third party webpage content is known as Contextual Advertising. The third form of advertising also deals with the placement of ads on third party Web pages, but the ads in this form are rich multimedia ads — image, video, audio, flash. The business model with rich media ads is slightly different from the ones with textual ads. These ads are also called banner ads, and this form of advertising is known as Display Advertising.Both Sponsored Search and Contextual Advertising involve retrieving relevant ads for different types of content (query and Web page). As ads are short and are mainly written to attract the user, retrieval of ads pose challenges like vocabulary mismatch between the query/content and the ad. Also, as the user's probability of examining an ad decreases with the position of the ad in the ranked list, it is imperative to keep the best ads at the top positions. Display Advertising poses several challenges including modeling user behaviour and noisy page content and bid optimization on the advertiser's side. Additionally, online advertising faces challenges like false bidding, click spam and ad spam. These challenges are prevalent in all forms of advertising. There has been a lot of research work published in different areas of CA in the last one and a half decade. The focus of this survey is to discuss the problems and solutions pertaining to the information retrieval, machine learning and statistics domain of CA. This survey covers techniques and approaches that deal with several issues mentioned above.Research in Computational Advertising has evolved over time and currently continues both in traditional areas (vocabulary mismatch, query rewriting, click prediction) and recently identified areas (user targeting, mobile advertising, social advertising). In this study, we predominantly focus on the problems and solutions proposed in traditional areas in detail and briefly cover the emerging areas in the latter half of the survey. To facilitate future research, a discussion of available resources, list of public benchmark datasets and future directions of work is also provided in the end.

Abstract

As soon as natural disaster events happen, users are eager to know more about them. However, search engines currently provide a ten blue links interface for queries related to such events. Relevance of results for such queries can be significantly improved if users are shown a structured summary of the fresh events related to such queries. This would not just reduce the number of user clicks to get the relevant information but would also help users get updated with more fine grained attribute-level information. Twitter is a great source that can be exploited for obtaining such fine-grained structured information for fresh natural disaster events. Such events are often reported on Twitter much earlier than on other news media. However, extracting such structured information from tweets is challenging because: 1. tweets are noisy and ambiguous; 2. there is no well defined schema for various types of natural disaster events; 3. it is not trivial to extract attribute-value pairs and facts from unstructured text; and 4. it is difficult to find good mappings between extracted attributes and attributes in the event schema. We propose algorithms to extract attribute-value pairs, and also devise novel mechanisms to map such pairs to manually generated schemas for natural disaster events. Besides the tweet text, we also leverage text from URL links in the tweets to fill such schemas. Our schemas are temporal in nature and the values are updated whenever fresh information flows in from human sensors on Twitter. Evaluation on ~58000 tweets for 20 events shows that our system can fill such event schemas with an F1 of ~0.6.

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.