Multi-way Relation Classification: Application to Protein-Protein Interactions

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 732–739, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Multi-way Relation Classification: Application to Protein-Protein Interactions Barbara Rosario SIMS UC Berkeley Berkeley, CA 94720 rosario@sims.berkeley.edu Marti A. Hearst SIMS UC Berkeley Berkeley, CA 94720 hearst@sims.berkeley.edu Abstract We address the problem of multi-way re- lation classification, applied to identifica- tion of the interactions between proteins in bioscience text. A major impediment to such work is the acquisition of appro- priately labeled training data; for our ex- periments we have identified a database that serves as a proxy for training data. We use two graphical models and a neu- ral net for the classification of the inter- actions, achieving an accuracy of 64% for a 10-way distinction between relation types. We also provide evidence that the exploitation of the sentences surrounding a citation to a paper can yield higher accu- racy than other sentences. 1 Introduction Identifying the interactions between proteins is one of the most important challenges in modern ge- nomics, with applications throughout cell biology, including expression analysis, signaling, and ratio- nal drug design. Most biomedical research and new discoveries are available electronically but only in free text format, so automatic mechanisms are needed to convert text into more structured forms. The goal of this paper is to address this difficult and important task, the extraction of the interactions between proteins from free text. We use graphical models and a neural net that were found to achieve high accuracy in the related task of extracting the re- lation types might hold between the entities “treat- ment” and “disease” (Rosario and Hearst, 2004). Labeling training and test data is time-consuming and subjective. Here we report on results using an existing curated database, the HIV-1 Human Protein Interaction Database1, to train and test the classifica- tion system. The accuracies obtained by the classi- fication models proposed are quite high, confirming the validity of the approach. We also find support for the hypothesis that the sentences surrounding ci- tations are useful for extraction of key information from technical articles (Nakov et al., 2004). In the remainder of this paper we discuss related work, describe the dataset, and show the results of the algorithm on documents and sentences. 2 Related work There has been little work in general NLP on trying to identify different relations between entities. Many papers that claim to be doing relationship recogni- tion in actuality address the task of role extraction: (usually two) entities are identified and the relation- ship is implied by the co-occurrence of these enti- ties or by some linguistic expression (Agichtein and Gravano, 2000; Zelenko et al., 2002). The ACE competition2 has a relation recognition subtask, but assumes a particular type of relation holds between particular entity types (e.g., if the two entities in question are an EMP and an ORG, then an employment relation holds between them; which type of employment relation depends on the type of entity, …

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