Sammenlign metoder

	Dokumentklyngning	GloVe Embeddings	Tekstklassificering
Fagområde	Tekstmining	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Oprindelsesår	—	2014	—
Ophavsperson	—	Pennington, Socher & Manning	—
Type	Unsupervised text-mining task	Static word-embedding model	Supervised NLP classification task
Oprindelig kilde	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Aliasser	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	GloVe, global vectors, GloVe Kelime Gömülmeleri	text categorization, document classification, topic classification, metin sınıflandırma
Relaterede	4	3	4
Resumé	Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000).	GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global word-word co-occurrence statistics gathered across an entire corpus. The resulting vectors place semantically related words close together and perform strongly on semantic analogy tasks.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.