Using Lexical and Relational Similarity to Classify Semantic Relations

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 621–629, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Using lexical and relational similarity to classify semantic relations Diarmuid ´O S´eaghdha Computer Laboratory University of Cambridge 15 JJ Thomson Avenue Cambridge CB3 0FD United Kingdom do242@cl.cam.ac.uk Ann Copestake Computer Laboratory University of Cambridge 15 JJ Thomson Avenue Cambridge CB3 0FD United Kingdom aac10@cl.cam.ac.uk Abstract Many methods are available for comput- ing semantic similarity between individ- ual words, but certain NLP tasks require the comparison of word pairs. This pa- per presents a kernel-based framework for application to relational reasoning tasks of this kind. The model presented here com- bines information about two distinct types of word pair similarity: lexical similarity and relational similarity. We present an efficient and flexible technique for imple- menting relational similarity and show the effectiveness of combining lexical and re- lational models by demonstrating state-of- the-art results on a compound noun inter- pretation task. 1 Introduction The problem of modelling semantic similarity be- tween words has long attracted the interest of re- searchers in Natural Language Processing and has been shown to be important for numerous applica- tions. For some tasks, however, it is more appro- priate to consider the problem of modelling sim- ilarity between pairs of words. This is the case when dealing with tasks involving relational or analogical reasoning. In such tasks, the chal- lenge is to compare pairs of words on the basis of the semantic relation(s) holding between the mem- bers of each pair. For example, the noun pairs (steel,knife) and (paper,cup) are similar because in both cases the relation N2 is made of N1 fre- quently holds between their members. Analogi- cal tasks are distinct from (but not unrelated to) other kinds of “relation extraction” tasks where each data item is tied to a specific sentence con- text (e.g., Girju et al. (2007)). One such relational reasoning task is the prob- lem of compound noun interpretation, which has received a great deal of attention in recent years (Girju et al., 2005; Turney, 2006; But- nariu and Veale, 2008). In English (and other languages), the process of producing new lexical items through compounding is very frequent and very productive. Furthermore, the noun-noun re- lation expressed by a given compound is not ex- plicit in its surface form: a steel knife may be a knife made from steel but a kitchen knife is most likely to be a knife used in a kitchen, not a knife made from a kitchen. The assumption made by similarity-based interpretation methods is that the likely meaning of a novel compound can be pre- dicted by comparing it to previously seen com- pounds whose meanings are known. This is a natural framework for computational techniques; there is also empirical evidence for similarity- based interpretation in human compound process- ing (Ryder, 1994; Devereux and Costello, 2007). This paper presents an approach to relational reasoning based on combining information about two …

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