A Context Centric Model for building a Knowledge advantage Machine Based on Personal Ontology Patterns

Throughout the industrial era societal advancement could be attributed in large part to introduction a plethora of electromechanical machines all of which exploited a key concept known as Mechanical Advantage. In the post-industrial era exploitation of knowledge is emerging as the key enabler for societal advancement. With the advent of the Internet and the Web, while there is no dearth of knowledge, what is lacking is an efficient and practical mechanism for organizing knowledge and presenting it in a comprehensible form appropriate for every context. This is the fundamental problem addressed by my dissertation.;We begin by proposing a novel architecture for creating a Knowledge Advantage Machine (KaM), one which enables a knowledge worker to bring to bear a larger amount of knowledge to solve a problem in a shorter time. This is analogous to an electromechanical machine that enables an industrial worker to bring to bear a large amount of power to perform a task thus improving worker productivity. This work is based on the premise that while a universal KaM is beyond the realm of possibility, a KaM specific to a particular type of knowledge worker is realizable because of the limited scope of his/her personal ontology used to organize all relevant knowledge objects.;The proposed architecture is based on a society of intelligent agents which collaboratively discover, markup, and organize relevant knowledge objects into a semantic knowledge network on a continuing basis. This in-turn is exploited by another agent known as the Context Agent which determines the current context of the knowledge worker and makes available in a suitable form the relevant portion of the semantic network. In this dissertation we demonstrate the viability and extensibility of this architecture by building a prototype KaM for one type of knowledge worker such as a professor

Keyphrase Generation: A Multi-Aspect Survey

Extractive keyphrase generation research has been around since the nineties, but the more advanced abstractive approach based on the encoder-decoder framework and sequence-to-sequence learning has been explored only recently. In fact, more than a dozen of abstractive methods have been proposed in the last three years, producing meaningful keyphrases and achieving state-of-the-art scores. In this survey, we examine various aspects of the extractive keyphrase generation methods and focus mostly on the more recent abstractive methods that are based on neural networks. We pay particular attention to the mechanisms that have driven the perfection of the later. A huge collection of scientific article metadata and the corresponding keyphrases is created and released for the research community. We also present various keyphrase generation and text summarization research patterns and trends of the last two decades.Comment: 10 pages, 5 tables. Published in proceedings of FRUCT 2019, the 25th Conference of the Open Innovations Association FRUCT, Helsinki, Finlan

Keywords at Work: Investigating Keyword Extraction in Social Media Applications

This dissertation examines a long-standing problem in Natural Language Processing (NLP) -- keyword extraction -- from a new angle. We investigate how keyword extraction can be formulated on social media data, such as emails, product reviews, student discussions, and student statements of purpose. We design novel graph-based features for supervised and unsupervised keyword extraction from emails, and use the resulting system with success to uncover patterns in a new dataset -- student statements of purpose. Furthermore, the system is used with new features on the problem of usage expression extraction from product reviews, where we obtain interesting insights. The system while used on student discussions, uncover new and exciting patterns. While each of the above problems is conceptually distinct, they share two key common elements -- keywords and social data. Social data can be messy, hard-to-interpret, and not easily amenable to existing NLP resources. We show that our system is robust enough in the face of such challenges to discover useful and important patterns. We also show that the problem definition of keyword extraction itself can be expanded to accommodate new and challenging research questions and datasets.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145929/1/lahiri_1.pd

Human-competitive automatic topic indexing

Topic indexing is the task of identifying the main topics covered by a document. These are useful for many purposes: as subject headings in libraries, as keywords in academic publications and as tags on the web. Knowing a document's topics helps people judge its relevance quickly. However, assigning topics manually is labor intensive. This thesis shows how to generate them automatically in a way that competes with human performance. Three kinds of indexing are investigated: term assignment, a task commonly performed by librarians, who select topics from a controlled vocabulary; tagging, a popular activity of web users, who choose topics freely; and a new method of keyphrase extraction, where topics are equated to Wikipedia article names. A general two-stage algorithm is introduced that first selects candidate topics and then ranks them by significance based on their properties. These properties draw on statistical, semantic, domain-specific and encyclopedic knowledge. They are combined using a machine learning algorithm that models human indexing behavior from examples. This approach is evaluated by comparing automatically generated topics to those assigned by professional indexers, and by amateurs. We claim that the algorithm is human-competitive because it chooses topics that are as consistent with those assigned by humans as their topics are with each other. The approach is generalizable, requires little training data and applies across different domains and languages