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The 1st DDIExtraction-2011 challenge task: Extraction of Drug-Drug Interactions from biomedical texts Challenge Task on Drug-Drug Interaction Extraction
, 2011
"... biomedical texts ..."
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Combining Tree Structures, Flat Features and Patterns for Biomedical Relation Extraction
"... 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 ..."
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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
Drug-drug Interaction Extraction Using Composite Kernels
"... 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 base ..."
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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.
Two Different Machine Learning Techniques for Drug-Drug Interaction Extraction
"... 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 ..."
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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
Exploiting Tree Kernels for High Performance Chemical Induced Disease Relation Extraction
"... Abstract Machine learning approaches based on supervised classification have emerged as effective methods for Biomedical relation extraction such as the Chemical-InducedDisease (CID) task. These approaches owe their success to a rich set of features crafted from the lexical and syntactic regulariti ..."
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Abstract Machine learning approaches based on supervised classification have emerged as effective methods for Biomedical relation extraction such as the Chemical-InducedDisease (CID) task. These approaches owe their success to a rich set of features crafted from the lexical and syntactic regularities in the text. Kernel methods are an effective alternative to manual feature engineering and have been successfully used in similar tasks such as text classification. In this paper, we study the effectiveness of tree kernels for Chemical-Disease relation extraction. Our experiments demonstrate that subset tree kernels increase the F-score to 61.7% as compared to 57.9% achieved with simple feature engineering. We also describe the strengths and shortcomings of tree kernel approaches for the CID relation extraction task.
EACL 2012 13th Conference of the European Chapter of the Association for Computational Linguistics
, 2012
"... 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 ..."
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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
Identifying Disease Definitions with a Correlation Kernel for Symptom Extractions from Text
"... Abstract—Since most health-related knowledge is created by experts, it is not easy for general public to access, understand, and utilize such knowledge in daily living. It would be most convenient and useful to a healthcare knowledge base that a user can easily start exploring from symptoms and arri ..."
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Abstract—Since most health-related knowledge is created by experts, it is not easy for general public to access, understand, and utilize such knowledge in daily living. It would be most convenient and useful to a healthcare knowledge base that a user can easily start exploring from symptoms and arrive at candidate diseases and eventually obtain knowledge for treatment and prevention. We have embarked on a project whose goal is to build such a healthcare knowledge base from text by using natural language processing and text mining techniques. This paper focuses on how definition sentences can be detected and describes a method of ranking sentences based on the degree to which they contain definitions of diseases, which should contain symptom information. While our work is basically to build a classifier that identifies definition sentences, the main contribution lies in the development of a new kernel method that utilizes correlations among different types of tokens. We evaluated our method to arrive at a conclusion that the proposed method can be very effective with a training data that is almost an order of magnitude smaller than the method of using dependency parser. Index Terms—symptom extraction; definition sentence; collo-cation; colligation; correlation kernel; I.
An Evaluation of the Effect of Automatic Preprocessing on Syntactic Parsing for Biomedical Relation Extraction
"... Relation extraction (RE) is an important text mining task which is the basis for further complex and advanced tasks. In state-of-the-art RE approaches, syntactic information obtained through parsing plays a crucial role. In the context of biomedical RE previous studies report usage of various automa ..."
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Relation extraction (RE) is an important text mining task which is the basis for further complex and advanced tasks. In state-of-the-art RE approaches, syntactic information obtained through parsing plays a crucial role. In the context of biomedical RE previous studies report usage of various automatic preprocessing techniques applied before parsing the input text. However, these studies do not specify to what extent such techniques improve RE results and to what extent they are corpus specific as well as parser specific. In this paper, we aim at addressing these issues by using various preprocessing techniques, two syntactic tree kernel based RE approaches and two different parsers on 5 widely used benchmark biomedical corpora of the protein-protein interaction (PPI) extraction task. We also provide analyses of various corpus characteristics to verify whether there are correlations between these characteristics and the RE results obtained. These analyses of corpus characteristics can be exploited to compare the 5 PPI corpora.
