Rajendra Pamula

Text summarization from legal documents: a survey

Enormous amount of online information, available in legal domain, has made legal text processing an important area of research. In this paper, we attempt to survey different text summarization techniques that have taken place in the recent past. We put special emphasis on the issue of legal text summarization, as it is one of the most important areas in legal domain. We start with general introduction to text summarization, briefly touch the recent advances in single and multi-document summarization, and then delve into extraction based legal text summarization. We discuss different datasets and metrics used in summarization and compare performances of different approaches, first in general and then focused to legal text. we also mention highlights of different summarization techniques. We briefly cover a few software tools used in legal text summarization. We finally conclude with some future research directions.

Electricity Consumption Forecasting Using Time Series Analysis

The demand for electricity has been continuously increasing over the years. To understand the future consumption, a good predictive model is entailed. The ARIMA models have been extensively used for time series prediction showing encouraging results. In this paper, an attempt is made on forecasting the electricity consumption using the ARIMA model. Using the mean absolute percentage error (MAPE) to measure forecast accuracy, the model was able to forecast with an error of 6.63%. Results shows that the ARIMA model has a potential to compete with existing techniques for electricity consumption forecast.

Fault classification and detection in wind turbine using Cuckoo-optimized support vector machine

Fault detection in wind turbine which is identified with complete system monitoring under multi-fault scenario is proposed. When a fault is detected, its types and location are recognized for easy maintenance. Fault in wind turbines is caused due to the high speed of gearbox, generator bearing and the failures occurred in various parts. In wind farm, wind turbine condition monitoring is used to reduce the maintenance cost and also improves the accuracy. Generally, in wind turbine gearbox condition monitoring using sensor is a gainful method to monitor wind turbine performance and fault. This paper nominates a method to decide the parameters for support vector machine (SVM) in wind turbine called Cuckoo search optimization (CSO). The combination of optimization technique with classification technique is evaluated. MATLAB platform was used to evaluate the various faults under fixed value and gain factor conditions. Comparing the accuracy with SVM, particle swarm optimized SVM and k-nearest neighbor, the proposed fault detection and fault isolation technique (CSO-SVM) is improved by 2.5%, 3.5% and 6.5%, respectively. The result shows the CSO model based on SVM algorithm accomplishes the most accurate fault detection than the past models.

Social Book Search: a survey

Social Book Search is a new area of social search. In the modern world everything is going to be amenable due to the web and social media. The web and social media give us access to a wealth of information, not only different in quantity but also in character. Traditional descriptions from professionals are now supplemented with user generated content. Although books have been the predominant source of information for centuries, the way we acquire, share, and publish information has changed and has been changing in fundamental ways due to the web. In the modern era, in order to purchase a book, the users are not only depend on the title, author name, publisher etc of the book available as controlled metadata but also on other aspects which include the reviews, editorial reviews etc available in different social media. Our primary focus in this survey is to describe the features of different social cataloging book sites as well as their recommendation based on books. How the online searching of books is useful to the user and up to what extent, and what are their aim are some of the issues we shall deal with. We will also discuss evolution of those techniques for Social Book Search presented over years at the Initiative for the Evaluation of XML Retrieval (INEX) and Conference and Labs of the Evaluation Forum (CLEF). To what extent the features and functionality of a social sharing platform influence the user behavior, is also discussed in the paper. This survey provides an overview of research done in the area of Social Book Search from perspective of Information Retrieval.

Design of a meta search system for legal domain

With proliferation of social media, legal data on the Web is increasing. Although online forums can only provide rudimentary legal assistance, people increasingly seek help online and often their legal information need gets partially met. Lack of easy natural language search amenity in these forums, nevertheless, deprives a novice user from quickly retrieving answers to similar questions which are previously asked. Also, they do not get a feel whether there are previous legal cases pertaining to the same issue or not. We introduce a meta search system with graphical easy-to-use interface that provides search facility on previously asked questions in the social media as well as on formal court proceedings. We used Terrier system in the back-end which indexes two different document collections (court proceedings and online legal forums). The Google-style search interface allows users to describe her legal information need. The retrieval is performed from the two indexes and best-matched documents are displayed. Navigation through hyperlinks enables the user to instantly access documents from either domain within the same interface in parallel. The objective is to make an automated meta search system that can provide holistic guidance in legal matters to the end user.

Social book search: Reranking based on document and query expansion with keyword Filtering

Online searching of books have gained astounding popularity worldwide. It has also attracted variety of researchers globally. Searching of books (e.g. Amazon.com, aNobii, LibraryThing etc) with the help of Social metadata(e.g. tags, reviews) and professional metadata (e.g. ISBN Number, Title, Publisher) is gradually becoming a sizzling hot topic under the aegis of Information Retrieval. In this paper, taking Social Book Search as an example, our experiment is divided into three folds: Firstly, in our experiment we expanded the corpus (i.e. Amazon/LibraryThing) by using other book search related catalog sites (e.g. lookupbyisbn.com and Goodreads) for indexing. Secondly, for query expansion, the terms of queries identified in the document, then those terms from the document are appended in our query for re-ranking. Finally, our proposed method extensively evaluated on CLEF 2015 Social Book Search datasets and has better performance compared to other state-of-the-art systems. Recently we got the better performance in CLEF 2016.