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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 Semantics

Frame 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:
CAUSE_HARM
Agent
Victim
Body_part
“Lee punched John in the eye”
Agent
APPLY_HEAT
Food
Heating_instrument
“She was frying eggs on a camp stove”
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 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.
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 Similarity
An example:
Impactor IMPACT
Impactee
“His back thudded against the wall”
“The rest of his body thumped against the front of the cage”
Measuring Similarity
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
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