BUILDING A SEMANTIC ROLE LABELING SYSTEM FOR VIETNAMESE SENTENCES

of the traditional 2-stage SRL model (i.e. bound-ary detection and argument classification) and onthe complete SRL task. We use three different fea-ture spaces: a set of standard attribute-value featuresand the AST and the EAST structures defined in3.4. Standard feature vectors can be combined witha p[r]

as <loves,john,(s\np)/np,1>, indicating the head ofthe functor, the head of the argument, the functor cat-egory and the argument slot. The second argument(the direct object) fills slot 2. This can be encoded as<loves,mary,(s\np)/np,2>. One of the potential ad-vantages to u[r]

crete structures. Technical Report UCSC-CRL-99-10, July.Zheng Ping Jiang, Jia Li, and Hwee Tou Ng. 2005. Se-mantic argument classification exploiting argumentinterdependence. In Proceedings of IJCAI-2005.Thorsten Joachims, Nello Cristianini, and John Shawe-Taylor. 2001. Composite kernels for h[r]

The extension of our work is to improve the per-formance of the entire semantic role labeling system using the grammar-driven tree kernel, including all four stages: pruning, semantic role identification, classification and post inference. In addition, a<[r]

beling (SRL) mainly focused on how to imple-ment SRL methods which are successful on En-glish. Similar to English, parsing is a standardpre-processing for Chinese SRL. Many featuresare extracted to represent constituents in the inputparses (Sun and Jurafsky, 2004; Xue, 2008; Dingand Ch[r]

2005 systems show a significant performance dropwhen the tested corpus, i.e. Brown, differs from237the training one (i.e. Wall Street Journal), e.g.(Toutanova et al., 2008). More recently, the state-of-art frame-based semantic role labeling systemdiscussed in (Johansson an[r]

We obtained best results with a model using acontext window of five words on either side of thetarget word, the cosine measure, and 2,000 vec-tor dimensions. The latter were the most com-mon context words (excluding a stop list of func-tion words). Their values were set to the ratio oft[r]

(non-argument) had often dominated other labels in the examples added to the training set. Lee et al. (2007) attacked another SRL learn-ing problem using self-training. Using Propbank instead of FrameNet, they aimed at increasing the performance of supervised SRL system by exploiting a[r]

tem, CLP(name2structure), in more detail in thefollowing section.In this introduction we described the particular-ities of biochemical terminology. Related work inthe area of processing these terms was overviewedand we gave the motivation for our own approach.After presenting our system