1. Progress report on Semantic Role Labeling
Abdul-Lateef Yussiff nd June, 2011
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2. Introduction
Semantic analysis of a sentence is a very important step towards understanding of natural Language
There are two approaches
Deep semantic analysis : given the current state of the art technology and human skills, it is too complex to follow this approach
Shallow semantic analysis:
Process of annotating the predicate argument structure in text with semantic labels (Jurafsky et al., 2005)
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3. Some widely recognized Semantic Roles
Description
Examples
Agent
Initiator of action
The pilot landed the plane as lightly as a feather.
Patient
Affected by action, undergoes change of state
John broke the window.
Theme
Entity moving or being “located”
John threw the ball.
The picture hangs above the fireplace
Experiencer
Perceives action but not in control
Lee noticed the cat slip through the partially open door
Beneficiary
For whose benefit action is performed
The Smiths rented an apartment for their son.
Instrument
Intermediary/means used to perform an action
He shot the wounded buffalo with a rifle.
Location
Place of object or action
The band played on the stage.
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4. PropBank Tags Arguments of a verb are labeled ARG0 to ARG5
ARG0 is the AGENT
ARG1 is the PATIENT
Adjunctive arguments
ARGM-Loc for locatives
ARGM-TMP for temporal
ARGM-MNR for manner
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5. Propbank’s representation of semantic role
[The pilot A0] landed predicate [the plane A1] as lightly as a feather ARG-M.
JohnA0 broke predicate [the window A1].
HeA0 shot predicate [the wounded buffaloA1] with a rifle.
[The acquisition ARG1] was completed predicate [in September ARG-TMP]
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6. Semantic Role Labeling and Machine Learning
The task is to select a constituent’s semantic role with respect to a given predicate.
To find the best semantic analysis of an entire sentence, it is recommended to extend beyond taking a decision on single constituent because the constituents are interdependent on each other.
The basic approach is to use a syntactic parser as input to the system.
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7. Goal of this Research
Goal is to use a machine learning sequence tagging approach to identify and label constituents with semantic roles
Conditional Random Field (CRF) was chosen because of its advantages over all other sequence tagging techniques.
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8. Experimental features
Word
POS
The syntactic class or category of the words
Phrase type
It indicates the syntactic categories of the phrase expressing the semantic roles.
Phrase type are extracted automatically from parse trees generated by the parser
Examples are Noun Phrase (NP), Prepositional Phrase (PP), etc.
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9. Example
Confidence in the pound is widely expected to take another sharp dive if trade figures for September, due for release, fail to show a substantial improvement from July and August’s near- record deficit.
ARG-0: Confidence in the pound
Predicate: take
ARG-1: Another Sharp dive
ARG-M: IF trades figures for September, due for release, fail to show a substantial improvement from July and August’ near-record deficit
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10. Sequence Tagging with Conditional Random Field
Training Stage
F1 F2 ….FN Label
Confidence in the pound is widely expected to take another sharp dive if trade figures for September, due for release, fail to show a substantial improvement from July and August’s near- record deficit.
Confidence NN B-NP B-A0
in IN B-PP I-A0
the DT B-NP I-A0
pound NN I-NP I-A0
is VBZ B-VP O
widely RB I-VP O
expected VBN I-VP O
to TO I-VP O
take VB I-VP PRED
Sequence Tagging with Conditional Random Field
SRL Output
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11. How to train the SRL with Mallet’s SimpleTagger
The Machine learning tools used in this exercises is Mallet, machine learniing toolkit provided by the Umarst.
To train the file, I used the following command
Java –cp “/root/lateef/mallet-2.0.6/class: /root/lateef/mallet-2.0.6//lib/mallet-deps.jar” cc.mallet.fst.SimpleTagger --train true –fully-connected false -- weights sparse --model-file wordmodel /root/lateef/semanticRole/test.txt
After executing the above command, it produces a model file named wordmodel
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12. After creating the training of the sample data which created the model file, we need to test and evaluate our sample model file with the following command
--model-file wordposmodel test1.txt
Rockwell International Corp. 's Tulsa unit said it signed a tentative agreement extending its contract with Boeing Co. to provide structural parts for Boeing's 747 jetliners.
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13. also RB (AM-DIS*AM-DIS) changing VBG (V*V) hands NNS (A2*A2)
Number of predicates: 26
East NNP A0
Rock NNP I-A0
also RB (AM-DIS*AM-DIS)
changing VBG (V*V)
hands NNS (A2*A2)
at IN AM-MNR
CD I-AM-MNR
yen NN I-AM-MNR
, , I-AM-MNR
up RB I-AM-MNR
from IN I-AM-MNR
CD I-AM-MNR
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14. Observation
I observed the result produced is not the same as the inputs data.
I am working on the source of the problems.
Reason why different output is coming out as the result of the input file?
I will try to resolve the problem before the next presentation, (the problem might be coming from the eclipse or the simpleTagger class itself.)
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15. Questions and Suggestions
Thank You
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