The Experts below are selected from a list of 261528 Experts worldwide ranked by ideXlab platform
Rada Mihalcea - One of the best experts on this subject based on the ideXlab platform.
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Multilingual Natural Language Processing
2012Co-Authors: Rada MihalceaAbstract:With rapidly growing online resources, such as Wikipedia, Twitter, or Facebook, there is an increasing number of Languages that have a Web presence, and correspondingly there is a growing need for effective solutions for multilingual natural Language Processing. In this talk, I will explore the hypothesis that a multilingual representation can enrich the feature space for natural Language Processing tasks, and lead to significant improvements over traditional solutions that rely exclusively on a monolingual representation. Specifically, I will describe experiments performed on three different tasks: word sense disambiguation, subjectivity analysis, and text semantic similarity, and show how the use of a multilingual representation can leverage additional information from the Languages in the multilingual space, and thus improve over the use of only one Language at a time. This is joint work with Samer Hassan and Carmen Banea.
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Graph-based Natural Language Processing and Information Retrieval
2011Co-Authors: Rada Mihalcea, Dragomir R RadevAbstract: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.
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Networks and Natural Language Processing
AI Magazine, 2008Co-Authors: Dragomir R Radev, Rada MihalceaAbstract:Over the last few years, a number of areas of natural Language Processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in Language Processing and try to explain how and why they work.
Dragomir R Radev - One of the best experts on this subject based on the ideXlab platform.
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Graph-based Natural Language Processing and Information Retrieval
2011Co-Authors: Rada Mihalcea, Dragomir R RadevAbstract: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.
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Networks and Natural Language Processing
AI Magazine, 2008Co-Authors: Dragomir R Radev, Rada MihalceaAbstract:Over the last few years, a number of areas of natural Language Processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in Language Processing and try to explain how and why they work.
Chengning Zhang - One of the best experts on this subject based on the ideXlab platform.
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Multilingual natural Language Processing environments
Proceedings Eighth Conference on Artificial Intelligence for Applications, 1992Co-Authors: R F Walters, Chengning ZhangAbstract:The authors describe the need for multilingual natural Language Processing and review previous work relating to the field. They present a proposal for a multilingual natural Language Processing environment, providing an overview of the total plan and describing specific components that have been implemented or are nearing the final design stage. Examples of several application areas are included in which this approach would be an effective tool for research and development
Paul Jacobs - One of the best experts on this subject based on the ideXlab platform.
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Natural-Language Processing
IEEE Expert-Intelligent Systems and their Applications, 1994Co-Authors: Paul JacobsAbstract:Processing natural Language such as English has always been one of\nthe central research issues of artificial intelligence, both because of\nthe key role Language plays in human intelligence and because of the\nwealth of potential applications. Many of the knowledge representation\nand inference techniques that have been applied successfully in\nknowledge-based systems were originally developed for Processing natural\nLanguage, but the Language-Processing applications themselves have\nalways seemed far from being realized. The special series on\nnatural-Language Processing is an attempt to bring Language Processing\nand its applications into focus-to demonstrate techniques that have\nrecently been applied to real-world problems, to identify research ripe\nfor practical exploitation, and to illustrate some promising\ncombinations of natural-Language Processing with other emerging\ntechnologies. Each of the four articles in the series provides some\ninsight into the state of the art and conveys the practical significance\nof recent research in the field
R F Walters - One of the best experts on this subject based on the ideXlab platform.
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Multilingual natural Language Processing environments
Proceedings Eighth Conference on Artificial Intelligence for Applications, 1992Co-Authors: R F Walters, Chengning ZhangAbstract:The authors describe the need for multilingual natural Language Processing and review previous work relating to the field. They present a proposal for a multilingual natural Language Processing environment, providing an overview of the total plan and describing specific components that have been implemented or are nearing the final design stage. Examples of several application areas are included in which this approach would be an effective tool for research and development
