Q3-D3-LSA

Arbeitspapier

QuantNet 1 is an integrated web-based environment consisting of different types of statistics-related documents and program codes. Its goal is creating reproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM) and latent semantic analysis (LSA). The LSA has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between documents. Our results show that different model configurations allow adapted similarity-based document clustering and knowledge discovery. In particular, different LSA configurations together with hierarchical clustering reveal good results under M3 evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under http://quantlet.de. The driving technology behind it is Q3-D3-LSA, which is the combination of 'GitHub API based QuantNet Mining infrastructure in R', LSA and D3 implementation.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: SFB 649 Discussion Paper ; No. 2016-049

Klassifikation und Themen

• Klassifikation: Wirtschaft
• Thema: QuantNet ; GitHub API ; text mining ; document clustering ; similarity ; semantic web ; generalized vector space model ; LSA ; visualization

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Borke, Lukas ; Härdle, Wolfgang Karl
• Ereignis: Veröffentlichung
• (wer): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (wo): Berlin
• (wann): 2016

Weitere Informationen

• Handle: http://hdl.handle.net/10419/148886
• Letzte Aktualisierung: 10.03.2025, 11:41 MEZ