Coherence Identification of Business Documents: Towards an Automated Message Processing System

This paper describes our recent efforts in developing a text segmentation technique in our business document management system. The document analysis is based upon a knowledge-based analysis of the documents’ contents, by automating the coherence identification process, without a full semantic understanding. In the technique, document boundaries can be identified by observing the shifts of segments from one cluster to another. Our experimental results show that the combination of the heterogeneous knowledge is capable to address the topic shifts. Given the increasing recognition of document structure in the fields of information retrieval as well as knowledge management, this approach provides a quantitative model and automatic classification of documents in a business document management system. This will beneficial to the distribution of documents or automatic launching of business processes in a workflow management system

Self-directedness, integration and higher cognition

In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm

NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd

Study on the use of metadata for digital learning objects in university institutional repositories (MODERI)

Metadata is a core issue for the creation of repositories. Different institutional repositories have chosen and use different metadata models, elements and values for describing the range of digital objects they store. Thus, this paper analyzes the current use of metadata describing those Learning Objects that some open higher educational institutions' repositories include in their collections. The goal of this work is to identify and analyze the different metadata models being used to describe educational features of those specific digital educational objects (such as audience, type of educational object, learning objectives, etc.). Also discussed is the concept and typology of Learning Objects (LO) through their use in University Repositories. We will also examine the usefulness of specifically describing those learning objects, setting them apart from other kind of documents included in the repository, mainly scholarly publications and research results of the Higher Education institution.En prens

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Indexing Languages for Information Management, a Promising Future or an Obsolete Resource?

Indexing languages have traditionally been an essential tool for organizing and retrieving documental information. The inclusion of indexing languages into the digital environment leads to new frontiers, but also new opportunities. This study shows the historical evolution of the indexing languages and its application in document management field. We analyze diverse trends for their digital use from two perspectives: their integration with other digital and linguistic resources, and the adjustment of them into the Web environment. Finally, there is an analysis of how these languages are used in the Web 2.0 and the incorporation of ontologies in the Semantic Web.This work was carried out within the framework of a research Project financed by the Spanish government (Ministerio de Educación y Ciencia, Secretaría de Estado de Universidades e Investigación, TIN 2007-67153)