Dissertation Information for Qi Li
MPACT Status: Fully Complete
Title: Searching for entities: When retrieval meets extraction
Abstract: Retrieving entities from inside of documents, instead of searching for documents or web pages themselves, has become an active topic in both commercial search systems and academic information retrieval research area. Taking into account information needs about entities represented as descriptions with targeted answer entity types, entity search tasks are to return ranked lists of answer entities from unstructured texts, such as news or web pages. Although it works in the same environment as document retrieval, entity retrieval tasks require finer-grained answers|entities|which need more syntactic and semantic analyses on germane documents than document retrieval. This work proposes a two-layer probability model for addressing this task, which integrates germane document identification and answer entity extraction. Germane document identification retrieves highly related germane documents containing answer entities, while answer entity extraction finds answer entities by utilizing syntactic or linguistic information from those documents. This work theoretically demonstrates the integration of germane document identification and answer entity extraction for the entity retrieval task with the probability model. Moreover, this probability approach helps to reduce the overall retrieval complexity while maintaining high accuracy in locating answer entities.
MPACT Scores for Qi Li
Advisors and Advisees Graph
Students under Qi Li