Simple Semantics in Topic Detection and Tracking
| Autor: | Marko Salmenkivi, Juha Makkonen, Helena Ahonen-Myka |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2004 |
| Předmět: |
topic detection and tracking
retrieval model Information retrieval Computer science Semantic analysis (machine learning) 02 engineering and technology Library and Information Sciences Ontology (information science) 16. Peace & justice Perceptron computer.software_genre Semantics Article Term (time) Information extraction 020204 information systems geographical ontology Similarity (psychology) Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing information extraction temporal expression computer Information Systems |
| Zdroj: | Information Retrieval |
| ISSN: | 1573-7659 1386-4564 |
| Popis: | Topic Detection and Tracking (TDT) is a research initiative that aims at techniques to organize news documents in terms of news events. We propose a method that incorporates simple semantics into TDT by splitting the term space into groups of terms that have the meaning of the same type. Such a group can be associated with an external ontology. This ontology is used to determine the similarity of two terms in the given group. We extract proper names, locations, temporal expressions and normal terms into distinct sub-vectors of the document representation. Measuring the similarity of two documents is conducted by comparing a pair of their corresponding sub-vectors at a time. We use a simple perceptron to optimize the relative emphasis of each semantic class in the tracking and detection decisions. The results suggest that the spatial and the temporal similarity measures need to be improved. Especially the vagueness of spatial and temporal terms needs to be addressed. |
| Databáze: | OpenAIRE |
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