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Tasks
-----

We propose several tasks for Arabic Semantic Labeling.  The tasks
will span both the WSD and Semantic Role labeling processes for this
evaluation. Both sets of tasks will be evaluated on data derived from
the same data set, the test set.

We propose 3 subtasks for WSD all of which will only have test data
for evaluation and trial data for formatting purposes. This will be
taken from the Arabic Treebank 3v2 text data, roughly 3000 words
long:

1.  The first task is to discover different senses in the data for
nouns and verbs without associating labels with those senses.
Therefore it is a sense discrimination task.

In this task the participants will be required to identify that the
different number of senses for nouns and verbs without associating
labels with those identified senses. The assumption is that word is
one of these senses identified. These senses will be derived from the
Arabic WordNet, which correspond to English WN 2.0. There will be two
levels of granularity, coarse and fine grain.


2.  The second task is to annotate all nouns and verbs in the data
with Arabic WordNet senses (provided with the test data, and also
accessible via the web at http://www.globalwordnet.org/AWN

All verbs and nouns in the data will need to be annotated with their
sense indices and/or offsets from Arabic WordNet


3.  The third task is to annotate all nouns and verbs in the data
with English wordnet senses

a.  In this task, the participants will be required to link the
Arabic nouns and verbs with their corresponding sense(s) in the
English WordNet 2.0

b.  An English translation corpus will be provided along with the
trial/test data

c.  A bilingual word list will also be provided.


We propose 2 subtasks for Semantic Role Labeling (SRL). These
subtasks will have trial, training and test data available for it:


4.  Identifying Arguments in a sentence

In this task, the participants are required to identify all the
constituents in a constituency tree that should be annotated with
argument roles related to some predetermined verbs


5.  Automatic annotations for all arguments

In this task, the participants are required to identify and label all
the constituents in a constituency tree that should be annotated with
both numbered argument roles and ARGM roles related to some
predetermined verbs


Data
----

The data will be Arabic Treebank 3 v.2 data which is newswire in
Modern Standard Arabic. The data will be presented in ascii encoding,
with the Buckwalter transliteration scheme. The data will be
unvowelised and tokenized according to the Arabic Treebank clitic
tokenization scheme. We will provide code for conversion of encoding
from UTF-8 and CP1256 to the Buckwalter transliteration scheme.
Moreover, we will provide code for the tokenization, POS tagging and
Base Phrase chunking of the Arabic text, a package can be downloaded
from http://www.cs.columbia.edu/~mdiab/ASVMTools.tar.gz.

We will only opt for 100 most frequent verbs in this set to draw
training, trial (for the semantic role labeling tasks) and test data
for the semantic role labeling and WSD tasks)

The data is syntactically and morphologically manually annotated. The
syntactic trees are constituency trees.

A preliminary version of the Arabic WordNet will be available


Evaluation metric
-----------------

SRL: Conlleval metrics of precision recall and f measure

WSD: Scorer 2.0 metrics of precision, recall and f-measure on both
coarse and fine grained sense distinctions.


Dates
-----
Nov 20th             Selecting the data
Feb 5th              Delivering trial data
March 1st            Delivering the training and test data
April 10th           Competition deadline

People
------
Mona Diab: Columbia University
Christiane Fellbaum: Princeton University
Mohamed Maamouri: LDC, University of Pennsylvania
Martha Palmer: University of Colorado, Boulder

 

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