I propose AdaptivePLSA for dynamic topic modeling with streams of documents. For the SIGIR proceedings, the learned topics give clear hints to the main research subjects. Next, I propose TopicTable, a visualization for presenting topics learned from document streams. TopicTable visualizes useful pieces of information, e.g., topics similarities and newly emerging words. It is effective as it provides clear hints to alien documents which were added to a test stream of documents. Next, I propose an approach for the disambiguation of social tags which have been added to documents by many users of a collaborative tagging system. This approach uncovers unobvious semantics of tags and visualizes topics which are learned from the tagged documents. Last, I apply bilingual topic modeling to NMR spectra and chemical constitutions of chemical compounds. The learned bilingual topics might be exploited by new approaches for data mining in chemical- and structure-databases of chemical compounds.