An exploration of entity models, collective classification and relation description (2004)
| Venue: | In Proceedings of KDD Workshop on Link Analysis and Group Detection |
| Citations: | 13 - 1 self |
BibTeX
@INPROCEEDINGS{Raghavan04anexploration,
author = {Hema Raghavan and James Allan and Andrew Mccallum},
title = {An exploration of entity models, collective classification and relation description},
booktitle = {In Proceedings of KDD Workshop on Link Analysis and Group Detection},
year = {2004},
pages = {33--3}
}
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Abstract
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.
