| 1 |
Accepts lengthy ambiguous complex Natural Language Queries
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| 2 |
Translates Natural Language queries into precise Boolean representations of the user's implied requirements for relevance of a document to query
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| 3 |
Produces summary-level semantic SFC (Subject Field Codes) vector representations of query and documents for quick filtering of large databases
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| 4 |
Catures Text Structure dimensions of time, source, state of completion, credibility, definiteness, intentionality and causality
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| 5 |
Fulfills Proper Noun requirements of queries by:
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Precise matching of focused Proper Noun requests
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Accurate Proper Noun category-level matching
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Expansion of categories to constituent members
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| 6 |
Provides both high recall and high precision via controlled expansion of Complex Nominals
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| 7 |
Evaluates Implicit and Explicit Semantics to assign similiarity between Query and Documents using its Integrated Matcher
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| 8 |
Predicts how many documents need to be viewed to acieve the user-specified level of recall via the Cut-Off Criterion
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