Abstract. Users often search for entities instead of documents and in this setting are willing to provide extra input, in addition to a query, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insight in the many ways of using these types of input for query modeling. We focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Our best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks. 1

Retrieving entities inside documents instead of documents or web pages themselves has become an active topic in both commercial search systems and academic information retrieval research. Our method of entity retrieval is based on a two-layer retrieval and extraction probability model (TREPM) for integrating document retrieval and entity extraction together. The document retrieval layer finds supporting documents from the corpus, and the entity extraction layer extracts the right entities from those supporting documents. We theoretically demonstrate that the entity extraction problem can be represented as TREPM model. The TREPM model can reduce the overall retrieval complexity while keeping high accuracy of locating target entities. The experiment is based on the document retrieval and entity extraction as well as the overall task. The preliminary results are promising and deserve for further exploration.

Knowledge Base Population (KBP) tasks, such as slot filling, show the particular importance of entity-oriented automatic relevant document acquisition. Rich, diverse and reliable relevant documents satisfy the fundamental require-ment that a KBP system explores the nature of an entity. Towards the bottleneck problem be-tween comprehensiveness and definiteness of acquisition, we propose a collaborative archiv-ing method. In particular we introduce topic modeling methodologies into entity biography profiling, so as to build a bridge between fuzzy and exact matching. On one side, we employ the topics in a small-scale high-quality relevant documents (i.e., exact matching re-sults) to summarize the life slices of a target entity (i.e., biography), and on the other side, we use the biography as a reliable reference material to detect new truly relevant docu-ments from a large-scale partially complete pseudo-feedback (i.e., fuzzy matching results). We leverage the archiving method to enhance slot filling systems. Experiments on KBP cor-pus show significant improvement over state-of-the-art. 1

Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insights in the many ways of using these types of input for query modeling. We focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Our best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks.