Semantic role labelling (SRL) has traditionally been viewed as a sentence-internal problem. However, it is clear that there is an interplay between local semantic argument structure and the surrounding discourse. In this shared task, we would like to take SRL of nominal and verbal predicates beyond the domain of isolated sentences by linking local semantic argument structures to the wider discourse context. In particular, we aim to find fillers for roles which are left unfilled in the local context (null instantiations, NIs). An example is given below, where the "charges" role ("arg2" in PropBank) of cleared is left empty but can be linked to murder in the previous sentence.

In a lengthy court case the defendant was tried for murder. In the end, he was cleared.

There will be two tasks, which will be evaluated independently (participants can choose to enter either or both):
For the Full Task the target predicates in the (test) data set will be annotated with gold standard word senses (frames). The participants have to:

• find the semantic arguments of the predicate (role recognition)
• label them with the correct role (role labelling)
• find links between null instantiations and the wider context (NI linking)

We will prepare new training and test data consisting of running text from the fiction domain. The data sets will be freely available. The training set for both tasks will be annotated with gold standard semantic argument structure (see for example the FrameNet full text annotation) and linking information for null instantiations. We aim to annotate the semantic argument structures both in FrameNet and PropBank style; participants can choose which one they prefer.

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• Test data release: March 26th
• Closing competition : April 2nd

1. Target verb WSD: to recognize whether there are some key verbs or verb phrases to describe two focused event contents in the sentence, and select suitable situation description formula for these recognized key verbs (or verb phrases), from a situation network lexicon. The input of the sub-task is a Chinese sentence annotated with correct word-segmentation and POS tags. Its output is the sense selection or disambiguation tags of the target verbs in the sentence.
2. Sentence SRL: to anchor different situation arguments with suitable syntactic chunks in the sentence, and annotate suitable syntactic constituent and functional tags for these arguments. Its input is a Chinese sentence annotated with correct word-segmentation, POS tags and the sense tags of the target verbs in the sentence. Its output is the syntactic chunk recognition and situation argument anchoring results.
3. Event detection: to detect and analyze the special event content through the interaction of target verb WSD and sentence SRL. Its input is a Chinese sentence annotated with correct word-segmentation and POS tags. Its output is a complete event description detected in the sentence (if it has a focused target verb).

今天/n(Today) 我/r(I) 在/p(at) 书店/n(bookstore) 买/v(buy) 了/u(-ed) 三/m(three) 本/q 新/a(new) 书/n(book) 。/w (Today, I bought three new books at the bookstore.)

After the first processing stage: target verb WSD, we find there is a possession-transferring verb ‘买/v(buy)’ in the sentence and select the following situation description formula for it:

买/v(buy): DO(x, P(x,y)) CAUSE have(x,y) AND NOT have(z,y) [P=buy]
Then, we anchor four situation arguments with suitable syntactic chunks in the sentence and obtain the following sentence SRL result:

今天/n(Today) [S-np 我/r(I) ]x [D-pp 在/p(at) 书店/n(bookstore) ]z [P-vp 买/v(buy) 了/u(-ed) ]Tgt [O-np 三/m(three) 本/q 新/a(new) 书/n(book) ]y 。/w[2]

Finally, we can get the following situation description for the sentence:

DO(x, P(x,y)) CAUSE have(x,y) AND NOT have(z,y) [x=我/r(I), y=三/m(three) 本/q 新/a(new) 书/n(book), z=书店/n(bookstore), P=买/v(buy)]

Organizers: Qiang Zhou (Tsinghua University, Beijing, China)
Web Site: http://www.ncmmsc.org/SemEval-2010-Task/

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