1. Based on “Semi-Supervised Semantic Role Labeling via Structural Alignment” by Furstenau and Lapata, 2011 Advisors: Prof. Michael Elhadad and Mr. Avi Hayoun By Efrat Hazani

2. Frame Semantics Frame Semantics is a theory that relates linguistic semantics to knowledge and experience The meaning of words depend on contexted experiences

3. For example: “I always eat cereal for breakfast” – breakfast : first meal of the day “Breakfast is served at any time” – breakfast: a particular combination of foods typically eaten at breakfast

4. What is a frame? Structured representation of concept Identifies the experience as a type, and gives structure and meaning to the relationships, objects and events within the experience A word represents a category of experience, and thus evokes a frame of semantic knowledge A word can evoke different frames

5. Frame Elements Frame evoking element (FEE) – the word (or lexical unit) which evokes the frame Frame elements (FEs) – words which have semantic roles in the frame Semantic roles describe the relations between a predicate and its arguments Semantic roles are independent from syntactic relations

6. For example: “Lee punched John in the eye” Agent CAUSE_HARM VictimBody_part “She was frying eggs on a camp stove” Agent Heating_instrument APPLY_HEAT Food

7. FrameNet A project building a lexical database of English that is both human- and machine-readable, based on annotating examples of how words are used in actual texts.

8. FrameNet – what is it good for? Provide a unique training dataset for semantic role labeling, used in applications such as: information extraction machine translation event recognition sentiment analysis

9. The Goal of the Project Create a larger collection of annotated sentences

10. The General Idea Input: ◦ A set L of sentences labeled with frames and roles (seed corpus) ◦ A set U of unlabeled sentences (expansion corpus) For every unlabeled sentence u : ◦ Find the most similar labeled sentence l ◦ Annotate u according to l ’s annotation. For every labeled sentence l: ◦ Return the k best newly annotated sentences according to l.

11. Measuring Similarity Sentences are represented by dependency graphs An alignment between two graphs: ◦ A partial injective function σ : M → N ∪ {} ◦ Domain M - a labeled graph ◦ Range N - an unlabeled graph ◦ x ∈ M is aligned to x’ ∈ N by σ, iff σ (x) = x’ Find a graph M with the best alignment to the unlabeled graph N

12. Measuring Similarity An example: “His back thudded against the wall” “The rest of his body thumped against the front of the cage” Impactor IMPACT Impactee

13. Calculating score for alignment σ : - the lexical similarity between x and σ (x) (a value between 0 and 1) - is the grammatical relation between x1 and x2 equal to the grammatical relation between σ (x1) and σ (x2) (0 or 1) Measuring Similarity

14. Calculating score for alignment σ : α - the relative weight of syntactic similarity compared to lexical similarity (optimal value ≈ 0.55) C - normalizing factor Measuring Similarity

15. Summary We want more sentences labeled with semantic roles Expand the set of annotated sentences by: ◦ For every unlabeled sentence, finding an optimal alignment with some labeled sentence ◦ Projecting annotation from the labeled sentence to the unlabeled sentence