Abstract. This paper describes a concept-based question analysis for an efficient document ranking. Our idea is that we can rank efficiently documents containing answers for questions when we use well-defined concepts because concepts occurred in questions of same answer type are similar. That is, we can retrieve more relevant documents if we know the syntactic and semantic role of each word or phrase in question. For each answer type, we define a concept rule which contains core concepts occurred in questions of that answer type. Concept-based question analysis is a process which tags concepts to morphological analysis result of a user's question, determines the answer type, and extracts untagged concepts from it using a matched concept rule. Empirical results show that our concept-based question analysis can rank documents more efficiently than any other conventional approach. Also, concept-based approach has additional merits that it is language universal model, and can be combined with arbitrary conventional approaches.
The high-level policy description language used for ubiquitous programming framework specifies context entity relations, as well as context-based access control and adaptation rules. Then the specification in the policy description language is translated into the code in a general-purpose language, which is to be used in ubiquitous environment. However, the inconsistencies and errors in the policy specification are all passed into the translated code, potentially resulting disastrous malfunction. This paper introduces a type system that checks the consistency of a policy specification so that the specification is free from type-related errors and inconsistencies.
Abstract. In this paper we present a Korean part-of-speech tagging system using resolution rules for individual ambiguous word. Our system resolves lexical ambiguities by common rules, rules for individual ambiguous word, and statistical approach. We built resolution rules for each word which has several distinct morphological analysis results with a view to enhancing tagging accuracy. Statistical approach based on Hidden Markov Model (HMM) is applied for ambiguous words that are not resolved by the rules. The experiment on the test set shows that the part-of-speech tagging system has high accuracy and broad coverage.
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