Analyzing Entities and Topics in News Articles Using Statistical Topic Models Export

@incollection{citeulike:781980, abstract = {Statistical language models can learn relationships between topics discussed in a document collection and persons, organizations and places mentioned in each document. We present a novel combination of statistical topic models and named-entity recognizers to jointly analyze entities mentioned (persons, organizations and places) and topics discussed in a collection of 330,000 New York Times news articles. We demonstrate an analytic framework which automatically extracts from a large collection: topics; topic trends; and topics that relate entities.}, author = {Newman, David and Chemudugunta, Chaitanya and Smyth, Padhraic and Steyvers, Mark}, citeulike-article-id = {781980}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/11760146_9}, citeulike-linkout-1 = {http://www.springerlink.com/content/pg65534266257l76}, doi = {10.1007/11760146_9}, journal = {Intelligence and Security Informatics}, keywords = {cs, nlp, topic-modeling}, month = {January}, pages = {93--104}, posted-at = {2006-08-01 23:16:36}, priority = {5}, title = {Analyzing Entities and Topics in News Articles Using Statistical Topic Models}, url = {http://dx.doi.org/10.1007/11760146_9}, volume = {3975}, year = {2006} }

TY - CHAP ID - citeulike:781980 L3 - citeulike-article-id:781980 N2 - Statistical language models can learn relationships between topics discussed in a document collection and persons, organizations and places mentioned in each document. We present a novel combination of statistical topic models and named-entity recognizers to jointly analyze entities mentioned (persons, organizations and places) and topics discussed in a collection of 330,000 New York Times news articles. We demonstrate an analytic framework which automatically extracts from a large collection: topics; topic trends; and topics that relate entities. JF - Intelligence and Security Informatics EP - 104 TI - Analyzing Entities and Topics in News Articles Using Statistical Topic Models VL - 3975 SP - 93 KW - cs KW - nlp KW - topic-modeling AU - Newman, David AU - Chemudugunta, Chaitanya AU - Smyth, Padhraic AU - Steyvers, Mark PY - 2006/01// UR - http://dx.doi.org/10.1007/11760146_9 ER -