Semi-SupervisedSemantic Role

Labeler

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

Frame SemanticsFrame Semantics is a theory that

relates linguistic semantics to knowledge and experience

The meaning of words depend on contexted experiences

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

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

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

For example:

“Lee punched John in the eye”

Agent

CAUSE_HARM Victi

mBody_part

“She was frying eggs on a camp stove”

Agent

Heating_instrument

APPLY_HEATFood

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.

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

The Goal of the Project

Create a larger collection of annotated sentences

The General IdeaInput:

◦ 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.

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

Measuring SimilarityAn example:

“His back thudded against the wall”“The rest of his body thumped against the front of the cage”

Impactor

IMPACT

Impactee

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

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

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