document: Structured entity identification and document categorization : two tasks with one joint model Traditionally , research in identifying structured entities in documents has proceeded independently of document categorization research . In this paper , we observe that these two tasks have much to gain from each other . Apart from direct references to entities in a database , such as names of person entities , documents often also contain words that are correlated with discriminative entity attributes , such age-group and income-level of persons . This happens naturally in many enterprise domains such as CRM , Banking , etc. . Then , entity identification , which is typically vulnerable against noise and incompleteness in direct references to entities in documents , can benefit from document categorization with respect to such attributes . In return , entity identification enables documents to be categorized according to different label-sets arising from entity attributes without requiring any supervision . In this paper , we propose a probabilistic generative model for joint entity identification and document categorization . We show how the parameters of the model can be estimated using an EM algorithm in an unsupervised fashion . Using extensive experiments over real and semi-synthetic data , we demonstrate that the two tasks can benefit immensely from each other when performed jointly using the proposed model .
doc_bio_tags: O B I O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O