Keyword extraction is an important technique for summarization, document clustering, Web page retrieval, document retrieval, text mining, and so on. By extracting significant keywords, we can easily identify the content which is easy to read and understand the relationship among documents. Keyword extraction is considered as one of the core technology for all automatic processing for text materials. This paper employs, Conditional Random Fields (CRF) for the task of extracting effective keywords that uniquely identify a document for Tamil using Machine Language Techniques. Keyword Extraction includes POS and Chunking process. Part Of Speech tagging and chunking are the elementary processing steps for any language processing process. Part of speech (POS) tagging is the procedure of labelling the annotation of syntactic categories for each word in the corpus. Chunking is the process of identifying and splitting the text into syntactically correlated word groups. Chunking process employs Conditional Random Field to segment the sentences. We have developed our own tagset for interpret the corpus, which is useful for training and testing the POS tag generator and the chunker. Results show that the Pos-tag enhanced keyword extraction model indeed may assist in automatic key word assignment and in fact performs significantly better than the original state-of-the-art keyword extractor.

The objective of this paper is to present a system for interrogating immense social media streams through analytical methodologies that characterize topics and events critical to tactical and strategic planning. First, we propose a conceptual framework for interpreting social media as a sensor network. Time-series models and topic clustering algorithms are used to implement this concept into a functioning analytical system. Next, we address two scientific challenges: 1) to understand, quantify, and baseline phenomenology of social media at scale, and 2) to develop analytical methodologies to detect and investigate events of interest. This paper then documents computational methods and reports experimental findings that address these challenges. Ultimately, the ability to process billions of social media posts per week over a period of years enables the identification of patterns and predictors of tactical and strategic concerns at an unprecedented rate through SociAL Sensor Analytics (SALSA).

Keywords are widely used to define queries within information retrieval (IR) systems as they are easy to define, revise, remember, and share. This chapter describes the rapid automatic keyword extraction (RAKE), an unsupervised, domain-independent, and language-independent method for extracting keywords from individual documents. It provides details of the algorithm and its configuration parameters, and present results on a benchmark dataset of technical abstracts, showing that RAKE is more computationally efficient than TextRank while achieving higher precision and comparable recall scores. The chapter then describes a novel method for generating stoplists, which is used to configure RAKE for specific domains and corpora. Finally, it applies RAKE to a corpus of news articles and defines metrics for evaluating the exclusivity, essentiality, and generality of extracted keywords, enabling a system to identify keywords that are essential or general to documents in the absence of manual annotations. Controlled Vocabulary Terms benchmark polls

This study investigates methods of automatically identifying and characterizing significant transitions in term usage over time. Within scientific literature, the occurrence of terms reflects the use of technologies and techniques as well as the study of specific species and materials. Transitions in terminology usage may be a result of vocabulary standardization or specialization in which terms are replaced with their shorter form. They may also be a result of new applications, combinations, alternatives, or interests that result in the appearance of new or existing terminology in unexpected contexts.

Sources of streaming information, such as news syndicates, publish information continuously. Information portals and news aggregators list the latest information from around the world enabling information consumers to easily identify events in the past 24 hours. The volume and velocity of these streams causes information from prior days to quickly vanish despite its utility in providing an informative context for interpreting new information. Few capabilities exist to support an individual attempting to identify or understand trends and changes from streaming information over time. The burden of retaining prior information and integrating with the new is left to the skills, determination, and discipline of each individual. In this paper we present a visual analytics system for linking essential content from information streams over time into dynamic stories that develop and change over multiple days. We describe particular challenges to the analysis of streaming information and present a fundamental visual representation for showing story change and evolution over time.

The goal of cyber security visualization is to help analysts increase the safety and soundness of our digital infrastructures by providing effective tools and workspaces. Visualization researchers must make visual tools more usable and compelling than the text-based tools that currently dominate cyber analysts' tool chests. A cyber analytics work environment should enable multiple, simultaneous investigations and information foraging, as well as provide a solution space for organizing data. We describe our study of cyber-security professionals and visualizations in a large, high-resolution display work environment and the analytic tasks this environment can support. We articulate a set of design principles for usable cyber analytic workspaces that our studies have brought to light. Finally, we present prototypes designed to meet our guidelines and a usability evaluation of the environment.

This paper presents and explores the application of a visualization and analysis tool - Juxter - as an interface for exploration of incidents described within the Worldwide Incidents Tracking System and describes several refinements that improve user interactions and aid identification of patterns and trends.

As computational resources continue to increase, the ability of computational simulations to effectively complement, and in some cases replace, experimentation in scientific exploration also increases. Today, large-scale simulations are recognized as an effective tool for scientific exploration in many disciplines including chemistry and biology. A natural side effect of this trend has been the need for an increasingly complex analytical environment. In this paper, we describe Northwest Trajectory Analysis Capability (NTRAC), an analytical software suite developed to enhance the efficiency of computational biophysics analyses. Our strategy is to layer higher-level services and introduce improved tools within the user’s familiar environment without preventing researchers from using traditional tools and methods. Our desire is to share these experiences to serve as an example for effectively analyzing data intensive large scale simulation data.

As computational resources continue to increase, the ability of computational simulations to effectively complement, and in some cases replace, experimentation in scientific exploration also increases. Today, large-scale simulations are recognized as an effective tool for scientific exploration in many disciplines including chemistry and biology. A natural side effect of this trend has been the need for an increasingly complex analytical environment. In this paper, we describe Northwest Trajectory Analysis Capability (NTRAC), an analytical software suite developed to enhance the efficiency of computational biophysics analyses. Our strategy is to layer higher-level services and introduce improved tools within the user's familiar environment without preventing researchers from using traditional tools and methods. Our desire is to share these experiences to serve as an example for effectively analyzing data intensive large scale simulation data.

Under the leadership of the US Department of Homeland Security (DHS), researchers at the Pacific Northwest National Laboratory (PNNL) established a research center focusing on the discipline of visual analytics in 2004. A year later, the center led a multidisciplinary panel representing academia, industry, and government to formally define directions and priorities for future research and development (R&D) for visual analytics tools. The R&D agenda, Illuminating the Path, defines the term visual analytics as ‘the science of analytical reasoning facilitated by interactive visual interfaces’. This article describes our progress to date in walking that path. We briefly describe the background of the subject, present major professional activities and accomplishments of its community, and highlight some of the ongoing R&D efforts being carried out by researchers at PNNL to fulfill the requirements and missions of a new discipline that promises to change the way we deal with today's information.

We present a system for analyzing conversational data. The system includes state-of-the-art natural language processing components that have been modified to accommodate the unique nature of conversational data. In addition, we leverage the added richness of conversational data by analyzing various aspects of the participants and their relationships to each other. Our tool provides users with the ability to easily identify topics or persons of interest, including who talked to whom, when, entities that were discussed, etc. Using this tool, one can also isolate more complex networks of information: individuals who may have discussed the same topics but never talked to each other. The tool includes a UI that plots information over time, and a semantic graph that highlights relationships of interest.