UCD-PN: Classification of Semantic Relations Between Nominals using WordNet and Web Counts

Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pages 374–377, Prague, June 2007. c⃝2007 Association for Computational Linguistics UCD-PN: Classification of Semantic Relations Between Nominals using WordNet and Web Counts Paul Nulty School of Computer Science and Informatics University College Dublin Dublin, Ireland paul.nulty@ucd.ie Abstract For our system we use the SMO implemen- tation of a support vector machine provided with the WEKA machine learning toolkit. As with all machine learning approaches, the most important step is to choose a set of features which reliably help to predict the label of the example. We used 76 features drawn from two very different knowledge sources. The first 48 features are boolean values indicating whether or not each of the nominals in the sentence are linked to cer- tain other words in the WordNet hypernym and meronym networks. The remaining 28 features are web frequency counts for the two nominals joined by certain common prepositions and verbs. Our system per- formed well on all but two of the relations; theme-tool and origin entity. 1 Introduction and Related Work This paper describes a system for participating in SemEval 2007 task 4; “Classification of Seman- tic Relations Between Nominals”. This SemEval task required systems to establish whether or not a particular semantic relation held between two nominals in a sentence. There were 7 semantic re- lations, with approximately 70 positive and 70 negative example sentences for each relation. There were approximately 70 examples in the test sets for each relation. This task is similar to the problem of determin- ing what semantic relation holds between the con- stituents of a noun-noun compound. Work in this area has used both statistical information about the frequencies of lexical patterns and hand-built knowledge databases such as WordNet and the- saura. In our system we combine these two knowl- edge sources and build a set of features to use as input to a Support Vector Machine learning algo- rithm. The use of hit counts from web search engines to obtain lexical information was introduced by Turney (2001). The idea of searching a large cor- pus for specific lexico-syntactic phrases to indicate a semantic relation of interest was first described by Hearst (1992). A lexical pattern specific enough to indicate a particular semantic relation is usually not very frequent, and using the web as a corpus alleviates the data sparseness problem. However, it also introduces some problems. The number of results returned is unstable as pages are created and deleted all the time, and the major search engines return only rounded frequency estimates and do not allow a very sophisticated query interface. Na- kov and Hearst (2005) examined the use of web- based n-gram frequencies for an NLP task and concluded that these issues do not greatly impact the interpretation of the results. Turney and Littman (2005) use web queries to the AltaVista search engine as the basis for their system to assign semantic relations to modifier- noun phrases. They use a set of 64 short preposi- …

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