Semantic Tagging and Chunk-Parsing in Dynamic Modeling

1. Semantic Tagging and Chunk-Parsing in Dynamic Modeling G. Fliedl, Ch. Kop, H.C. Mayr, A. Salbrechter, G. Weber, Ch. Winkler

2. About the Project /1 Motivation /1 FPA Validation order customer article Die Auftragsab- teilung bearbeitet die Posten Text Mapping into UML natural language sentence parsing or tagging Interpretation to collect KCPM entries

3. KCPM entries thing-, operation-, cooperation-,connection- types, pre- and post conditions notions: DFD petri-nets EPK notions: use case diagram notions: interaction diagram notions: activity- diagram notions: state charts notions: UML-object- model, ER, HERM, … About the Project /2 Motivation /2 heuristics support the mapping

4. Conceptual Predesign (KCPM) • Modeling notions of KCPM Dynamic • operation type, conditions, cooperation type, thing type • (+ meta attributes) • task must be executed (operation type) • someone is responsible to execute the task (actor / thing type) • tasks manipulates things (parameter / thing type) (e.g. thing types perform tasks, tasks update/read thing types, thing types are recipients) • a set of tasks can only be executed under specific pre-conditions and the execution leads to post-conditions (cooperation type) • things can be involved in conditions (involved thing types) (e.g. things have a specific state expressed by these conditions)

5. Tagging /1 • FEATURES • POS-Tagger • NP and PP Chunking • Verb classification (12 Verbclasses and Subclasses) • Word stemming • Morphological analysis • Tree representation in XML • Integration in MS Word

6. Tagging /2

7. Mapping /1 Mapping Idea (From NL to KCPM) • Fundamental relationships between the respective phrases have to found out based on the - verb category, - PAS and the semantic roles – Agent (AG), Theme (TH), Goal (GO)… - sentence mode (active voice, passive voice) - type of clause - identification of noun phrases - articles … • After that, these relationships are interpreted (mapped) to the KCPM dynamic model

8. Mapping /2 Example 1 Der Auftragtrifft ein (The order comes in) • Ad (1) Fundamental relationships • Ergative Verb  1 internal Argument • Internal Argument can be found either • The first N3 tag before the verb (default/normal case) • Directly after the verb (N3 is not in the topic position) • Ad (2) Interpretation • An ergative verb is a candidate for a condition • The internal Argument is the involved thing type of the condition comes in eV TH order

9. Mapping /3 Example 2 Die Auftragsabteilungprüft jeden Artikel des Auftrags (The order departmentchecks each article of the order) • Ad (1) Fundamental relationships • Binary Agentive Verb •  1 external Argument (AG) •  1 internal Argument (TH) • freedom of topicalisation • semantic roles are hard to identify. • cases of the nouns are used (articles, quantifiers) • Ad (1) Fundamental relationships • An agent verb is a candidate for an operation • Arguments are the involved thing types of the operation checks tvag2 AG TH order department article Poss. order

10. Conclusion We try to find the balance between free sentences which we still analyzable and interpretable Extended POS-Tagging is a possible solution to that To find semantic Relationship then remains to the interpretation part Extended Tagging has to combined with deep parsing in some cases