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Frequently Asked Questions about Natural Language Processing - Second Edition
No full textThis is an attempt to put together a list of frequently (and not so frequently) asked questions about Natural Language Processing and their answers. This document is in no way perfect or complete or 100% accurate. In no way should the maintainer be responsible for damage resulting directly or indirectly from using information in this FAQ. The FAQ originated from Mark Kantrowitz's FAQ on AI. Some questions in the present document come directly from Mark's original FAQ (available at http://www.faqs.org). This FAQ is maintained by Dragomir R. Radev from Columbia University.Please send me all your comments, suggestions, corrections, additions,and such to my e-mail address: [email protected]
A survey of graphs in natural language processing
No full textGraphs are a powerful representation formalism that can be applied to a variety of aspects related to language processing. We provide an overview of how Natural Language Processing problems have been projected into the graph framework, focusing in particular on graph construction – a crucial step in modeling the data to emphasize the phenomena targeted
Graph-based natural language processing and information retrieval / Rada Mihalcea, Dragomir Radev.
No full textIncludes bibliographical references (pages 179-190) and index.Book fair 2013.viii, 192 pages :"This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval"--"Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms"-
Some Inequalities for Normal Operators in Hilbert Spaces
No full textSome inequalities for normal operators in Hilbert spaces are given. For this purpose, some results for vectors in inner product spaces due to Buzano, Dunkl-Williams, Hile, Goldstein-Ryff-Clarke, Dragomir-S´andor and the author are employed
CST Bank: A Corpus for the Study of Cross-document Structural Relationships
No full textjahna,radev,zhuzhang§ Clusters of multiple news stories related to the same topic exhibit a number of interesting properties. For example, when documents have been published at various points in time or by different authors or news agencies, one finds many instances of paraphrasing, information overlap and even contradiction. The current paper presents the Cross-document Structure Theory (CST) Bank, a collection of multi-document clusters in which pairs of sentences from different documents have been annotated for cross-document structure theory relationships. We will describe how we built the corpus, including our method for reducing the number of sentence pairs to be annotated by our hired judges, using lexical similarity measures. Finally, we will describe how CST and the CST Bank can be applied to different research areas such as multi-document summarization. 1
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