Abstract. Detection of drug-drug interaction (DDI) is crucial for identification of adverse drug effects. In this paper, we present a range of new composite kernels that are evaluated in the DDIExtraction2011 challenge. These kernels are computed using different combinations of tree and feature based kernels. The best result that we obtained is an F1 score of 0.6370 which is higher than the already published result on this same corpus.

Abstract. Detection of drug-drug interaction (DDI) is an important task for both patient safety and efficient health care management. In this paper, we explore the combination of two different machine-learning approaches to extract DDI: (i) a feature-based method using a SVM classifier with a set of features extracted from texts, and (ii) akernelbased method combining 3 different kernels. Experiments conducted on the DDIExtraction2011 challenge corpus (unified format) show that our method is effective in extracting DDIs with 0.6398 F1. Keywords: Drug-Drug Interaction, machine learning, feature-based method, kernel-based method, tree kernel, shallow linguistic kernel. 1

Kernel based methods dominate the current trend for various relation extraction tasks including protein-protein interaction (PPI) extraction. PPI information is critical in understanding biological processes. Despite considerable efforts, previously reported PPI extraction results show that none of the approaches already known in the literature is consistently better than other approaches when evaluated on different benchmark PPI corpora. In this paper, we propose a novel hybrid kernel that combines (automatically collected) dependency patterns, trigger words, negative cues, walk features and regular expression patterns along with tree kernel and shallow linguistic kernel. The proposed kernel outperforms the exiting state-of-the-art approaches on the BioInfer corpus, the largest PPI benchmark corpus available. On the other four smaller benchmark corpora, it performs either better or almost as good as the existing approaches. Moreover, empirical results show that the proposed hybrid kernel attains considerably higher precision than the existing approaches, which indicates its capability of learning more accurate models. This also demonstrates that the different types of information that we use are able to complement each other for relation extraction. 1

Computational Linguistics. We are happy that despite strong competition from other Computational Linguistics events and economic turmoil in many European countries, this EACL is comparable to the successful previous ones, both in terms of the number of papers submitted and in terms of attendance. We have a strong scientific program, including ten workshops, four tutorials, a demos session and a student research workshop. I am convinced that you will appreciate our program. What does a General Chair at EACL have to do? Not much, it turns out. My job was to act as a liaison between the local organizing team, the scientific committees, and the EACL board, and to give advice when needed. Looking back at the thousands of e-mails I was copied on reminded me of the Jerome K. Jerome quote: ”I like work. I can sit and look at it for hours”. It has been an enjoyable experience to cooperate with the many people who made this conference happen, and to see them work. I have learned a lot from them. The Program Committee at an ACL conference is a trained army of Area Chairs, Program Committee members, and additional reviewers. Mirella Lapata and Lluís Màrquez commanded this particular one. It is thanks to the voluntary peer reviewing work, year after year, of this large group of people, formed by the top researchers in our field, that you will find a high-quality program. It is thanks to Mirella and Lluís