UNITN: Part-Of-Speech Counting in Relation Extraction

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 198–201, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics UNITN: Part-Of-Speech Counting in Relation Extraction Fabio Celli University of Trento Italy fabio.celli@unitn.it Abstract This report describes the UNITN system, a Part-Of-Speech Context Counter, that par- ticipated at Semeval 2010 Task 8: Multi- Way Classification of Semantic Relations Between Pairs of Nominals. Given a text annotated with Part-of-Speech, the system outputs a vector representation of a sen- tence containing 20 features in total. There are three steps in the system’s pipeline: first the system produces an estimation of the entities’ position in the relation, then an estimation of the semantic relation type by means of decision trees and finally it gives a predicition of semantic relation plus entities’ position. The system ob- tained good results in the estimation of en- tities’ position (F1=98.3%) but a critically poor performance in relation classification (F1=26.6%), indicating that lexical and se- mantic information is essential in relation extraction. The system can be used as an integration for other systems or for pur- poses different from relation extraction. 1 Introduction and Background This technical report describes the UNITN system (a Part-Of-Speech Context Counter) that partici- pated to Semeval 2010 Task 8: Multi-Way Clas- sification of Semantic Relations Between Pairs of Nominals (see Hendrickx et al., 2009). A different version of this system based on Part-Of-Speech counting has been previously used for the auto- matic annotation of three general and separable se- mantic relation classes (taxonomy, location, asso- ciation) obtaining an average F1-measure of 0.789 for english and 0.781 for italian, see Celli 2010 for details. The organizers of Semeval 2010 Task 8 provided ten different semantic relation types in context, namely: • Cause-Effect (CE). An event or object leads to an effect. Example: Smoking causes can- cer. • Instrument-Agency (IA). An agent uses an instrument. Example: Laser printer. • Product-Producer (PP). A producer causes a product to exist. Example: The growth hor- mone produced by the pituitary gland. • Content-Container (CC). An object is phys- ically stored in a delineated area of space, the container. Example: The boxes contained books. • Entity-Origin (EO). An entity is coming or is derived from an origin (e.g., position or material). Example: Letters from foreign countries. • Entity-Destination (ED). An entity is mov- ing towards a destination. Example: The boy went to bed. • Component-Whole (CW). An object is a component of a larger whole. Example: My apartment has a large kitchen. • Member-Collection (MC). A member forms a nonfunctional part of a collection. Exam- ple: There are many trees in the forest. • Message-Topic (CT). An act of communica- tion, whether written or spoken, is about a topic. Example: The lecture was about se- mantics. • Other. The entities are related in a way that do not fall under any of the previous men- tioned classes. Example: Batteries stored in a discharged state are susceptible to freezing. 198 The task was to predict, given a …

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