1. Transition Network Grammars for Natural Language Analysis - W. A. Woods In-Su Yoon Pusan National University School of Electrical and Computer Engineering Pusan National University

2. What’s a grammar? A grammar contains the knowledge about “legal” syntactic structure, represented as rewrite rules. The grammar defines the language. Pusan National University

3. Here’s a very simple example S  NP VP VP  VERB NP NP  ART NOUN NP  POSS NOUN My dog ate a frog. NOUN  frog | dog ART  a VERB  ate POSS  my Pusan National University

4. Parse tree Pusan National University S NPVP NOUNVERB NOUN POSSNP ART mydogateafrog The tree notation is difficult to compute with directly, so we can convert the representation into more useful: (S (NP (POSS my) (NOUN dog)) (VP (VERB ate) (NP (ART a) (NOUN frog))))

5. From grammars to transition nets As grammars get more rules, they become difficult to understand and computationally more demanding. We can make our jobs easier by converting the grammar to a more convenient representation known as a FSM or transition network (TN). Pusan National University

6. Transition Network Pusan National University S0S1S2 NPVP VP0VP1VP2 VERBNP NP0NP1NP2 ARTNOUN NP3NP4NP5 POSSNOUN NP0NP1NP2 ART NOUN POSS S0S1S2 ART NOUN POSS S3S4S5 ART NOUN POSS VERB When the lexicon gets really big, drawing them takes forever!

7. Why do we want recursive rules in our grammar? Natural languages allow us to express an infinite range of ideas using a finite set of rules and symbols. The boy drowned. The boy with the raft drowned. The boy with the raft near the island drowned. The boy with the raft near the island in the ocean drowned. The boy.... Pusan National University

8. From transition nets to recursive transition nets The ability to push a destination on a stack, jump to a subnetwork, and return to the pushed destination. Pusan National University

9. A sample recursive transition network(1) Pusan National University The boy broke the window with a hammer.

10. A sample recursive transition network(2) Pusan National University

11. RTNs are not enough RTNs are interesting from a theoretical standpoint but are not of much use themselves. From a computational perspective, a black box that accepts English input and just say “yes” or “no” doesn’t buy us much. What we need is a black box that records the structure of the input as well as providing an evaluation of syntactic correctness. Pusan National University

12. Augmented Transition Network (ATN) (1) Adding procedures to the arcs of the RTN. These procedures are then performed when the corresponding arcs are traversed. The resulting network is called an “augmented transition network” (ATN). Pusan National University

13. Augmented Transition Network (ATN) (2) One of the things we can do with these added procedures or augmentations is to store information in registers when arcs are traversed. To record the structure of the input, we add an action to each arc which stores the word that was processed while traversing that arc in an appropriate register. Pusan National University

14. Register assignment (1) If we now process the noun phrase “the vicious dog” with the ATN. We’ll have made the following register assignments: ART = the ADJS = vicious NOUN = dog Pusan National University

15. Register assignment (2) When we take the “pop” arc from this network, we can then accumulate the register contents into a larger structure that we call NP : (NP (ART the) (ADJS vicious) (NOUN dog)) Pusan National University

16. “the vicious dog ate the wimpy frog” the resulting structure would be : (S(SUBJ(NP(ART the) (ADJS vicious) (NOUN dog))) (VERB ate) (OBJ(NP(ART the) (ADJS wimpy) (NOUN frog)))) Pusan National University

17. Conclusion The great majority of natural language processing (NLP) systems in actual use in the world today are based on the ATN formalism. They are often used as front-ends or interfaces to database systems for question answering. One often cited example of a system that followed this approach successfully is a program called LUNAR, written by William Woods. They are often designed to perform both the syntactic and semantic analysis at the same time. Pusan National University