Search (9 results, page 1 of 1)

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Abstract

This chapter presents a broad survey of the cause-effect relation, with particular emphasis an how the relation is expressed in text. Philosophers have been grappling with the concept of causation for centuries. Researchers in social psychology have found that the human mind has a very complex mechanism for identifying and attributing the cause for an event. Inferring cause-effect relations between events and statements has also been found to be an important part of reading and text comprehension, especially for narrative text. Though many of the cause-effect relations in text are implied and have to be inferred by the reader, there is also a wide variety of linguistic expressions for explicitly indicating cause and effect. In addition, it has been found that certain words have "causal valence"-they bias the reader to attribute cause in certain ways. Cause-effect relations can also be divided into several different types.

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Abstract

The Internet is a giant semiotic system. It is a massive collection of Peirce's three kinds of signs: icons, which show the form of something; indices, which point to something; and symbols, which represent something according to some convention. But current proposals for ontologies and metadata have overlooked some of the most important features of signs. A sign has three aspects: it is (1) an entity that represents (2) another entity to (3) an agent. By looking only at the signs themselves, some metadata proposals have lost sight of the entities they represent and the agents - human, animal, or robot - which interpret them. With its three branches of syntax, semantics, and pragmatics, semiotics provides guidelines for organizing and using signs to represent something to someone for some purpose. Besides representation, semiotics also supports methods for translating patterns of signs intended for one purpose to other patterns intended for different but related purposes. This article shows how the fundamental semiotic primitives are represented in semantically equivalent notations for logic, including controlled natural languages and various computer languages

Series

Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence

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Series

Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence

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Series

Lecture notes in computer science; vol.1867: Lecture notes on artificial intelligence

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Abstract

Automatic identification of semantic relations in text is a difficult problem, but is important for many applications. It has been used for relation matching in information retrieval to retrieve documents that contain not only the concepts but also the relations between concepts specified in the user's query. It is an integral part of information extraction-extracting from natural language text, facts or pieces of information related to a particular event or topic. Other potential applications are in the construction of relational thesauri (semantic networks of related concepts) and other kinds of knowledge bases, and in natural language processing applications such as machine translation and computer comprehension of text. This chapter examines the main methods used for identifying semantic relations automatically and their application in information retrieval and information extraction.

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Abstract

Begriffsorientiertes Information Retrieval bedarf einer informationswissenschaftlichen Theorie der Begriffe sowie der semantischen Relationen. Ein Begriff wird durch seine Intension und Extension sowie durch Definitionen bestimmt. Dem Problem der Vagheit begegnen wir durch die Einführung von Prototypen. Wichtige Definitionsarten sind die Begriffserklärung (nach Aristoteles) und die Definition über Familienähnlichkeiten (im Sinne Wittgensteins). Wir modellieren Begriffe als Frames (in der Version von Barsalou). Die zentrale paradigmatische Relation in Wissensordnungen ist die Hierarchie, die in verschiedene Arten zu gliedern ist: Hyponymie zerfällt in die Taxonomie und die einfache Hyponymie, Meronymie in eine ganze Reihe unterschiedlicher Teil-Ganzes-Beziehungen. Wichtig für praktische Anwendungen ist die Transitivität der jeweiligen Relation. Eine unspezifische Assoziationsrelation ist bei den angepeilten Anwendungen wenig hilfreich und wird durch ein Bündel von generalisierbaren und fachspezifischen Relationen ersetzt. Unser Ansatz fundiert neue Optionen der Anwendung von Wissensordnungen in der Informationspraxis neben ihrem "klassischen" Einsatz beim Information Retrieval: Erweiterung von Suchanfragen (Anwendung der semantischen Nähe), automatisches Schlussfolgern (Anwendung der terminologischen Logik in Vorbereitung eines semantischen Web) und automatische Berechnungen (bei Funktionalbegriffen mit numerischen Wertangaben).

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Pages

S.22-33

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Date

1.12.2002 11:12:22

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Date

11.12.2019 19:00:22