We present a model for the automatic semantic analysis of requirements elicitation documents. Our target semantic representation employs live sequence charts, a multi-modal visual language for scenariobased programming, which can be directly translated into executable code. The architecture we propose integrates sentencelevel and discourse-level processing in a generative probabilistic framework for the analysis and disambiguation of individual sentences in context. We show empirically that the discourse-based model consistently outperforms the sentence-based model when constructing a system that reflects all the static (entities, properties) and dynamic (behavioral scenarios) requirements in the document.
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Semantic Parsing Using Content and Context: A Case Study from Requirements Elicitation
Semantic Scholar · Computer Science · 2014
Abstract
We present a model for the automatic semantic analysis of requirements elicitation documents. Our target semantic representation employs live sequence charts, a multi-modal visual language for scenariobased programming, which can be directly translated into executable code. The architecture we propose integrates sentencelevel and discourse-level processing in a generative probabilistic framework for the analysis and disambiguation of individual sentences in context. We show empirically that the discourse-based model consistently outperforms the sentence-based model when constructing a system that reflects all the static (entities, properties) and dynamic (behavioral scenarios) requirements in the document.
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