Proposal for an IMLS Collection Registry and Metadata Repository

The University of Illinois at Urbana-Champaign proposes to design, implement, and research a collection-level registry and item-level metadata repository service that will aggregate information about digital collections and items of digital content created using funds from Institute of Museum and Library Services (IMLS) National Leadership Grants. This work will be a collaboration by the University Library and the Graduate School of Library and Information Science. All extant digital collections initiated or augmented under IMLS aegis from 1998 through September 30, 2005 will be included in the proposed collection registry. Item-level metadata will be harvested from collections making such content available using the Open Archives Initiative Protocol for Metadata Harvesting (OAI PMH). As part of this work, project personnel, in cooperation with IMLS staff and grantees, will define and document appropriate metadata schemas, help create and maintain collection-level metadata records, assist in implementing OAI compliant metadata provider services for dissemination of item-level metadata records, and research potential benefits and issues associated with these activities. The immediate outcomes of this work will be the practical demonstration of technologies that have the potential to enhance the visibility of IMLS funded online exhibits and digital library collections and improve discoverability of items contained in these resources. Experience gained and research conducted during this project will make clearer both the costs and the potential benefits associated with such services. Metadata provider and harvesting service implementations will be appropriately instrumented (e.g., customized anonymous transaction logs, online questionnaires for targeted user groups, performance monitors). At the conclusion of this project we will submit a final report that discusses tasks performed and lessons learned, presents business plans for sustaining registry and repository services, enumerates and summarizes potential benefits of these services, and makes recommendations regarding future implementations of these and related intermediary and end user interoperability services by IMLS projects.unpublishednot peer reviewe

Inferring Narrative Causality between Event Pairs in Films

To understand narrative, humans draw inferences about the underlying relations between narrative events. Cognitive theories of narrative understanding define these inferences as four different types of causality, that include pairs of events A, B where A physically causes B (X drop, X break), to pairs of events where A causes emotional state B (Y saw X, Y felt fear). Previous work on learning narrative relations from text has either focused on "strict" physical causality, or has been vague about what relation is being learned. This paper learns pairs of causal events from a corpus of film scene descriptions which are action rich and tend to be told in chronological order. We show that event pairs induced using our methods are of high quality and are judged to have a stronger causal relation than event pairs from Rel-grams

Computational Models (of Narrative) for Literary Studies

In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive Science (CS) has approached the problem of narrative understanding by means of computational systems. Narrative, in fact, is an ubiquitous element in our everyday activity and the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence. However, despite the fact that - from an historical standpoint - narrative (and narrative structures) have been an important topic of investigation in both these areas, a more comprehensive approach coupling them with narratology, digital humanities and literary studies was still lacking. With the aim of covering this empty space, in the last years, a multidisciplinary effort has been made in order to create an international meeting open to computer scientist, psychologists, digital humanists, linguists, narratologists etc.. This event has been named CMN (for Computational Models of Narrative) and was launched in the 2009 by the MIT scholars Mark A. Finlayson and Patrick H. Winston1

Spontaneous Analogy by Piggybacking on a Perceptual System

Most computational models of analogy assume they are given a delineated source domain and often a specified target domain. These systems do not address how analogs can be isolated from large domains and spontaneously retrieved from long-term memory, a process we call spontaneous analogy. We present a system that represents relational structures as feature bags. Using this representation, our system leverages perceptual algorithms to automatically create an ontology of relational structures and to efficiently retrieve analogs for new relational structures from long-term memory. We provide a demonstration of our approach that takes a set of unsegmented stories, constructs an ontology of analogical schemas (corresponding to plot devices), and uses this ontology to efficiently find analogs within new stories, yielding significant time-savings over linear analog retrieval at a small accuracy cost.Comment: Proceedings of the 35th Meeting of the Cognitive Science Society, 201

Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective

This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates portability across different institutions and data systems by incorporating an enriched Common Data Model (CDM) to standardize necessary data elements. It utilizes UMLS to perform domain adaptation when integrating generic domain NLP tools. It also features stand-off annotations that are specified by positional reference to the original document. We built an interval tree based search engine to efficiently query and retrieve the stand-off annotations by specifying positional requirements. We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with inline annotations. We experimented with our system on several NLP facilitated tasks including computational phenotyping for lymphoma patients and semantic relation extraction for clinical notes. These experiments showcased the broader applicability and utility of LAPNLP.Comment: 6 pages, accepted by IEEE BIBM 2018 as regular pape

Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text

The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding. This paper introduces the task of identifying if a desire expressed by a subject in a given short piece of text was fulfilled. We propose various unstructured and structured models that capture fulfillment cues such as the subject's emotional state and actions. Our experiments with two different datasets demonstrate the importance of understanding the narrative and discourse structure to address this task