ISTI@SemEval-2 Task 8: Boosting-Based Multiway Relation Classification

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 218–221, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics ISTI@SemEval-2 Task #8: Boosting-Based Multiway Relation Classification Andrea Esuli, Diego Marcheggiani, Fabrizio Sebastiani Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche 56124 Pisa, Italy firstname.lastname@isti.cnr.it Abstract We describe a boosting-based supervised learning approach to the “Multi-Way Clas- sification of Semantic Relations between Pairs of Nominals” task #8 of SemEval- 2. Participants were asked to determine which relation, from a set of nine relations plus “Other”, exists between two nomi- nals, and also to determine the roles of the two nominals in the relation. Our participation has focused, rather than on the choice of a rich set of features, on the classification model adopted to de- termine the correct assignment of relation and roles. 1 Introduction The “Multi-Way Classification of Semantic Rela- tions between Pairs of Nominals” (Hendrickx et al., 2010) we faced can be seen as the composition of two sub-tasks: 1. Determining which relation r, from a set of relations R (see Table 1), exists between two entities e1 and e2. 2. Determining the direction of the relation, i.e., determining which of r(e1, e2) or r(e2, e1) holds. The set R is composed by nine “semantically determined” relations, plus a special Other rela- tion which includes all the pairs which do not be- long to any of the nine previously mentioned rela- tions. The two novel aspects of this task with respect to the similar task # 4 of SemEval-2007 (Girju et al., 2007) (“Classification of Semantic Relations be- tween Nominals”) are (i) the definition of the task as a “single-label” classification task and (ii) the 1 Cause-Effect 2 Instrument-Agency 3 Product-Producer 4 Content-Container 5 Entity-Origin 6 Entity-Destination 7 Component-Whole 8 Member-Collection 9 Message-Topic Table 1: The nine relations defined for the task. need of determining the direction of the relation (i.e., Item 2 above). The classification task described can be formal- ized as a single-label (aka “multiclass”) text clas- sification (SLTC) task, i.e., as one in which exactly one class must be picked for a given object out of a set of m available classes. Given a set of objects D (ordered pairs of nom- inals, in our case) and a predefined set of classes (aka labels, or categories) C = {c1, . . . , cm}, SLTC can be defined as the task of estimating an unknown target function Φ : D → C, that describes how objects ought to be classified, by means of a function ˆΦ : D → C called the classi- fier1. In the relation classification task which is the object of this evaluation, the set C of classes is composed of 19 elements, i.e., the nine relations of Table 1, each one considered twice because it may take two possible directions, plus Other. 2 The learner As the learner for our experiments we have used a boosting-based learner called MP-BOOST (Esuli et al., 2006). Boosting is among …

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