Traditional information retrieval typically represents data using a bag of words; data mining typically uses a highly structured database representation. This paper explores the middle ground using a representation which we term entity models, in which questions about structured data may be posed and answered, but the complexities and task-specific restrictions of ontologies are avoided. An entity model is a language model or word distribution associated with an entity, such as a person, place or organization. Using these perentity language models, entities may be clustered, links may be detected or described with a short summary, entities may be collectively classified, and question answering may be performed. On a corpus of entities extracted from newswire and the Web, we group entities by profession with 90 % accuracy, improve accuracy further on the task of classifying politicians as liberal or conservative using collective classification and conditional random fields, and answer questions about “who a person is ” with mean reciprocal rank (MRR) of 0.52. 1.

We propose entity language models, a probabilistic representation of the language used to describe a named entity (person, organization, or location). The model is purely statistical and constructed from snippets of text surrounding mentions of an entity. We evaluate the effectiveness of entity models in three tasks: fact-based question answering, classification into pre-defined groups, and description of the relationship between two entities. The results on all tasks are promising.

Language models assign probabilities to strings of symbols. Their interpretation is reviewed and applied to text classi cation. A language recogniser is constructed from Bayes' theorem and a simple bigram model. This provides