Abstract. Several phrase chunkers have been proposed over the past few years. Some state-of-the-art chunkers achieved better performance via integrating external resources, e.g., parsers and additional training data, or combining multiple learners. However, in many languages and domains, such external materials are not easily available and the combination of multiple learners will increase the cost of training and testing. In this paper, we propose a mask method to improve the chunking accuracy. The experimental results show that our chunker achieves better performance in comparison with other deep parsers and chunkers. For CoNLL-2000 data set, our system achieves 94.12 in F rate. For the base-chunking task, our system reaches 92.95 in F rate. When porting to Chinese, the performance of the base-chunking task is 92.36 in F rate. Also, our chunker is quite efficient. The complete chunking time of a 50K words document is about 50 seconds. 1

We explore here the application of Gleaner, an Inductive Logic Programming approach to learning in highly-skewed domains, to the Learning Language in Logic 2005 biomedical information-extraction challenge task. We create and describe a large number of background knowledge predicates suited for this task. We find that Gleaner outperforms standard Aleph theories with respect to recall and that additional linguistic background knowledge improves recall. 1.

Abstract. In this paper, we proposed an efficient and accurate text chunking system using linear SVM kernel and a new technique called mask method. Previous researches indicated that systems combination or external parsers can highlight the chunking performance. However the cost of constructing multiclassifiers is even higher than developing a single processor. Besides, the use of external resources will complicate the original tagging process. To remedy these problems, we employ richer features and propose a masked-based method to solve unknown word problem to enhance system performance. In this way, no external resources and complex heuristics are necessary for the chunking system. The experiments show that when training with the CoNLL-2000 chunking data set, our system achieves 94.12 in F (β) rate and 94.21 with SVM POS-tagger. Furthermore, our chunker is quite efficient since it adopts linear kernel SVM. The turn around tagging time on CoNLL-2000 testing data is less than 52 seconds. 1

This report presents a general overview of the network related activities at this site and specific reports for the postdoc, the PhD student, the local coordinator and others. An overview of the training activities concludes this section