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The REVERE project: experiments with the application of probabilistic NLP to systems engineering

The REVERE project: experiments with the application of probabilistic NLP to systems engineering. Paul Rayson 1 , Luke Emmet 2 , Roger Garside 1 and Pete Sawyer 1 1 Lancaster University and 2 Adelard. Abstract.

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The REVERE project: experiments with the application of probabilistic NLP to systems engineering

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  1. The REVERE project: experiments with the application of probabilistic NLP to systems engineering Paul Rayson1, Luke Emmet2, Roger Garside1 and Pete Sawyer1 1 Lancaster University and 2Adelard

  2. Abstract • Despite natural language's well-documented shortcomings as a medium for precise technical description, its use in software-intensive systems engineering remains inescapable. This poses many problems for engineers who must derive problem understanding and synthesise precise solution descriptions from free text. This is true both for the largely unstructured textual descriptions from which system requirements are derived, and for more formal documents, such as standards, which impose requirements on system development processes. This paper describes experiments that we have carried out in the REVERE project to investigate the use of probabilistic natural language processing techniques to provide systems engineering support.

  3. Natural language products of the systems engineering process Requirements Specification Systems Engineering Process Test plans

  4. Natural language inputs to the systems engineering process User interview transcripts Systems Engineering Process Standards documents User manuals

  5. The REVERE project

  6. Robust NLP Large free text corpora Training data NLP toolbox REVERE

  7. Syntactic and Semantic analysis • POS analysis using CLAWS • Hybrid tagger using HMM • Error rate of 1.5% • Semantic analysis • General sense field of words and idioms • Investigating applicability to technical domain • Wmatrix retrieval tool • Frequency profiling and KWIC

  8. Air Traffic Control • Ethnographic studies at ATC centre • Verbatim transcripts of observations and interviews with controllers • Unstructured reports • 103 pages

  9. Role Analysis

  10. Corpus analysis Log-likelihood Semantic Word sense (and examples from the text) tag 3366 S7.1 power, organising (‘controller’, ‘chief’) 2578 M5 flying (‘plane’, ‘flight’, ‘airport’) 988 O2 general objects (‘strip’, ‘holder’, ‘rack’) 643 O3 electrical equipment (‘radar’, ‘blip’) 535 Y1 science and technology (‘PH’) 449 W3 geographical terms (‘Pole Hill’, ‘Dish Sea’) 432 Q1.2 paper documents and writing (‘writing’, ‘written’, ‘notes’) 372 N3.7 measurement (‘length’, ‘height’, ‘distance’, ‘levels’, ‘1000ft’) 318 L1 life and living things (‘live’) 310 A10 indicating actions (‘pointing’, ‘indicating’, ‘display’) 306 X4.2 mental objects (‘systems’, ‘approach’, ‘mode’, ‘tactical’, ‘procedure’) 290 A4.1 kinds, groups (‘sector’, ‘sectors’)

  11. Corpus Analysis (2)

  12. A Standards Document • National standard for procurement of safety-critical military systems • 21,000 words • Strongly structured and highly stylised

  13. POS analysis

  14. Role Analysis

  15. Conclusions • Rapid analyses of complex and voluminous free text • Support tools for system engineers • Shallow analysis to help focus attention • Two experiments: ATC & Standards Doc

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