Document retrieval

About: Document retrieval is a research topic. Over the lifetime, 6821 publications have been published within this topic receiving 214383 citations.

Document retrieval system and question answering system

Abstract: A document retrieval system capable of obtaining information requested by the user with a high degree of accuracy. In this system, the query input section 102 receives query input by the user. The keyword extraction section 104 analyzes the input query and extracts keywords. The keyword type assignment section 106 decides the type of each extracted keyword and assigns a keyword type. The question type decision section 108 decides the question type. The keyword classification section 110 classifies the keywords to which the keyword types are assigned into a major type and minor type with reference to the keyword classification rules stored in the keyword classification rule storage section 112. The document retrieval section 114 searches a document collection stored in the document storage section 116 using the classified keyword groups and obtains the document of the retrieved result.

System and method for retrieving documents responsive to a given user's role and scenario

Abstract: A document retrieval and navigational system and method based on a user's role, scenario, and context in a given context or scenario. A database is provided which has a plurality of documents which are stored therein. Each document having metadata defined by at least one predetermined tag and corresponding value which is assigned thereto. Selection criteria is received and corresponds to a user scenario. The received selection criteria is compared to the metadata of each document to determine if there is a document match. In the case that a document match is found, a retrieval report is generated for presentation on the output device which includes each document match.

Text Retrieval Computers

Abstract: The hardware required for efficient text retrieval differs from that required for retrieval of formatted data. Here is an examination of such hardware, particularly term comparators.

Multimedia input in automated image annotation and content-based retrieval

Abstract: This research explores the interaction of linguistic and photographic information in an integrated text/image database. By utilizing linguistic descriptions of a picture (speech and text input) coordinated with pointing references to the picture, we extract information useful in two aspects: image interpretation and image retrieval. In the image interpretation phase, objects and regions mentioned in the text are identified; the annotated image is stored in a database for future use. We incorporate techniques from our previous research on photo understanding using accompanying text: a system, PICTION, which identifies human faces in a newspaper photograph based on the caption. In the image retrieval phase, images matching natural language queries are presented to a user in a ranked order. This phase combines the output of (1) the image interpretation/annotation phase, (2) statistical text retrieval methods, and (3) image retrieval methods (e.g., color indexing). The system allows both point and click querying on a given image as well as intelligent querying across the entire text/image database.