Inquery System Overview 1. Description of Final System 1.1. Approach 1.2. Processing Flow Country Recognizer: for Each Mention of a Country Company Name Recognizer: for Each Citation of A

(which includes MCC as a subcontractor), has focused on the following goals: Improving the eeectiveness of information retrieval techniques for large, full-text databases, Improving the eeectiveness of routing techniques appropriate for long-term information needs, and Demonstrating the eeectiveness of these retrieval and routing techniques for Japanese full text databases 5]. Our general approach to achieving these goals has been to use improved representations of text and information needs in the framework of a new model of retrieval. This model uses Bayesian networks to describe how text and queries should be used to identify relevant documents 7, 4, 8]. Retrieval (and routing) is viewed as a proba-bilistic inference process which compares text representations based on diierent forms of linguistic and statistical evidence to representations of information needs based on similar evidence from natural language queries and user interaction. Learning techniques are used to modify the initial queries both for short-term and long-term information needs (relevance feedback and routing, respectively). This approach (generally known as the inference net model and implemented in the INQUERY system 2]) emphasizes retrieval based on combination of evidence. Diierent text representations (such as words, phrases, paragraphs, or manually assigned keywords) and diier-ent versions of the query (such as natural language and Boolean) can be combined in a consistent probabilistic framework. This type of \data fusion" has been known to be eeective in the information retrieval context for a number of years, and was one of the primary motivations for developing the inference net approach. Another feature of the inference net approach is the ability to capture complex structure in the network representing the information need (i.e. the query). A practical consequence of this is that complex Boolean queries can be evaluated as easily as natural language queries and produce ranked output. It is also possible to represent \rule-based" or \concept-based" queries in the same probabilistic framework. This has led to us concentrating on automatic analysis of queries and techniques for enhancing queries rather than on in-depth analysis of the documents in the database. In general, it is more eeec-tive (as well as eecient) to analyze short query texts than millions of document texts. The results of the query analysis are represented in the INQUERY query language which contains a number of operators, such as #SUM, #AND, #OR, #NOT, #PHRASE, and #SYN. These operators implement diierent methods of combining evidence. Some of the speciic research issues we are …