TOPIC DETECTION Sample Clauses

TOPIC DETECTION. 6.1. LDA topic models The MALLET tool22 has been used to train an LDA topic model (Blei et al. 2003) for English with focus on distinguishing the following topics:
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TOPIC DETECTION. Currently the KEA keyphrases are used to determine document-specific topics. More specific topic models are useful for analysing the contents of large document collections. In addition, advanced topic models such as Latent Dirichlet Allocation (LDA) [Blei et al 2003] can be used to determine multiple topics per document, and Pachinko Model Allocation (XXX) has the added benefit of providing relations between topics, see [Xxx et al 2006]. These topic models are also available in a public domain toolkit. The MALLET toolkit36 contains implementations of Latent Dirichlet Allocation, Pachinko Allocation, and Hierarchical LDA. 34 xxxx://xxxxxxxx.xxxxxxxxxx.xxx/semLabeler. For a Question/Answering application using the semanticRoleLabeller see [Xxxxxx et al 2014]. 35 UIMA integration: xxxx://xxxxxxxxxxxxx.xxx/artifact/de.tudarmstadt.ukp.dkpro.core/de.tudarmstadt.ukp.dkpro.cor e.clearnlp-asl/1.6.2 36McCallum, Xxxxxx Xxxxxxxx (2002). MALLET: A Machine Learning for Language Toolkit. xxxx://xxxxxx.xx.xxxxx.xxx The disadvantage of topic models such as LDA and XXX is that they are computed relative to a document collection and do not adapt to new topics. An exception is Relevance Modelling (RM), see [Xxxxxxxx et al 2001]. RM does not need any training data but builds on the query alone. See also [Xx et al 2009] for a comparison of the different topic models when used in an information retrieval task. Their finding is that in document retrieval Relevance Modelling performs best. Within the EUMSSI context, LDA and XXX are promising as well, since they provide fine-grained topic information for individual documents which supports text mining tasks.
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