1. Dependency Parser for Swedish Project for EDA171 by Jonas Pålsson Marcus Stamborg

2. Dependency Grammar  Describes relations between words in a sentence  A relation is between a head and its dependent(s)  All words have a head except the root of a sentence Thebigbrown beaver brown The beaver big

3. Dependency Parsing  Find the links that connects words using a computer.  Different algorithms exist.  Nivre's parser has reported the best results for swedish.

4. Nivre's Parser  Extension to Shift-Reduce.  Adds arcs between input and stack.  Produces a dependency graph using the following actions:  Shift - moves the input to the stack.  Reduce - pops the stack.  Left arc - creates an arc from input to stack.  Right arc - creates an arc from stack to input.

5. More about actions Nivre, J. (2004)

6. Corpus  Talbanken05 – modernized and computerized version of Talbanken76  Modified for use in CoNNL-X Shared Task  Training set is about 11500 sentences  We used a test set containing about 300 sentences Example from the corpus: 1Jag_POPO_2SS__ 2tycker_VVVV_0ROOT__ 3det_POPO_2OO__

7. How we did it  Collect data  Build model  Parse ARFFBuilder Trainer Parser Train Corpus Data Trained Classifier Test Corpus with relations Test Corpus

8. Collect data – Gold Standard Parsing  Build Weka compatible data file (arff).  Determining the action sequence from an annotated corpus is possible using the following rules. (Gold Standard Parsing)  If input has stack as head -> Right Arc  else if stack has input as head -> Left Arc  else if arc exists between input and any word in stack -> Reduce  else Shift

9. Train classifier  Weka 3 – Data mining software  C4.5 (J48) – Extension to the ID3 algorithm. Generates decision trees  Uses features derived from the current state of the parser  Outputs a trained classifier used by the parser to decide the next action

10. Parse using trained classifier  Uses the trained classifier to determine the head for each word in a sentence  Uses Nivre's algorithm with action decided by the classifier  Calculates the score as

11. Features  All features describe the current state of the parser  1 st set – Input and stack  2 nd set – Input, stack and children.  3 rd set – Input, stack and previous input.  4 th set – Input, stack, children and previous input.  We only used POS in the feature sets  Using lexical values actually decreased performance  For every set we used constraints to model valid actions in the current state of the parser

12. Results Scores using features: Stack_n_POS, Input_n_POS, Children Scores using features: Stack_n_POS, Input_n_POS

13. Results cont. Scores using features: Stack_n_POS, Input_n_POS, Children, Previous_Input_POS Scores using features: Stack_n_POS, Input_n_POS, Previous_Input_POS

14. Conclusions  Lexical values didn’t do much. Score even became worse. Might be better with different classifying algorithm or different test corpus  Previous input word was a very effective feature, probably the single best addition from only stack and input  Difficult to find optimal feature set

15. Future improvements  Try other features  Siblings  Use LEX on specific words  More words from original input string  Simulations to find the optimum feature set  Use SVM instead of C4.5

16. Thank you for listening  More to come in the report