There are many possible different semantic relationships between nominals . Classification of such relationships is an important and difficult task ( for example , the well known noun compound classification task is a special case of this problem ) . We propose a novel pattern clusters method for nominal relationship ( NR ) classification . Pattern clusters are discovered in a large corpus independently of any particular training set , in an unsupervised manner . Each of the extracted clusters corresponds to some unspecified semantic relationship . The pattern clusters are then used to construct features for training and classification of specific inter-nominal relationships . Our NR classification evaluation strictly follows the ACL SemEval-07 Task 4 datasets and protocol , obtaining an f-score of 70.6 , as opposed to 64.8 of the best previous work that did not use the manually provided WordNet sense disambiguation tags . Automatic extraction and classification of semantic relationships is a major field of activity , of both practical and theoretical interest . A prominent type of semantic relationships is that holding between nominals1 . For example , in noun compounds many different semantic relationships are encoded by the same simple form ( Girju et al . , 2005 ) : ' dog food ' denotes food consumed by dogs , while ' summer morn-ing ' denotes a morning that happens in the summer . These two relationships are completely different semantically but are similar syntactically , and distinguishing between them could be essential for NLP applications such as question answering and machine translation . Relation classification usually relies on a training set in the form of tagged data . To improve results , some systems utilize additional manually constructed semantic resources such as WordNet ( WN ) ( Beamer et al . , 2007 ) . However , in many domains and languages such resources are not available . Furthermore , usage of such resources frequently requires disambiguation and connection of the data to the resource ( word sense disambiguation in the case of WordNet ) . Manual disambiguation is unfeasible in many practical tasks , and an automatic one may introduce errors and greatly degrade performance . It thus makes sense to try to minimize the usage of such resources , and utilize only corpus contexts in which the relevant words appear . A leading method for utilizing context information for classification and extraction of relationships is that of patterns ( Hearst , 1992 ; Pantel and Pennacchiotti , 2006 ) . The standard classification process is to find in an auxiliary corpus a set of patterns in which a given training word pair co-appears , and use pattern-word pair co-appearance statistics as features for machine learning algorithms . In this paper we introduce a novel approach , based on utilizing pattern clusters that are prepared separately and independently of the training set . We do not utilize any manually constructed resource or any manual tagging of training data beyond the cor-rect classification , thus making our method applicable to fully automated tasks and less domain and language dependent . Moreover , our pattern clustering algorithm is fully unsupervised . Our method is based on the observation that while each lexical pattern can be highly ambiguous , several patterns in conjunction can reliably define and represent a lexical relationship . Accordingly , we construct pattern clusters from a large generic corpus , each such cluster potentially representing some important generic relationship . This step is done without accessing any training data , anticipating that most meaningful relationships , including those in a given classification problem , will be represented by some of the discovered clusters . We then use the training set to label some of the clusters , and the labeled clusters to assign classes to tested items . One of the advantages of our method is that it can be used not only for classification , but also for further analysis and retrieval of the observed relationships2 . The semantic relationships between the components of noun compounds and between nominals in general are not easy to categorize rigorously . Several different relationship hierarchies have been proposed ( Nastase and Szpakowicz , 2003 ; Moldovan et al . , 2004 ) . Some classes , like Container-Contained , Time-Event and Product-Producer , appear in several classification schemes , while classes like Tool-Object are more vaguely defined and are subdivided differently . Recently , SemEval-07 Task 4 ( Girju et al . , 2007 ) proposed a benchmark dataset that includes a subset of 7 widely accepted nominal relationship ( NR ) classes , allowing consistent evaluation of different NR classification algorithms . In the SemEval event , 14 research teams evaluated their algorithms using this benchmark . Some of the teams have used the manually annotated WN labels provided with the dataset , and some have not . We evaluated our algorithm on SemEval-07 Task 4 data , showing superior results over participating algorithms that did not utilize WordNet disambiguation tags . We also show how pattern clusters can be used for a completely unsupervised classification of the test set . Since in this case no training data is used , this allows the automated discovery of a potentially unbiased classification scheme . Section 2 discusses related work , Section 3 outlines the pattern clustering algorithm , Section 4 details three classification methods , and Sections 5 and 6 describe the evaluation protocol and results . Relationship classification is known to improve many practical tasks , e.g. , textual entailment ( Tatu and Moldovan , 2005 ) . We have presented a novel framework for relationship classification , based on pattern clusters prepared as a standalone resource independently of the training set . Our method outperforms current state-of-the-art algorithms that do not utilize WordNet tags on Task 4 of SemEval-07 . In practical situations , it would not be feasible to provide a large amount of such sense disambiguation tags manually . Our method also shows competitive performance compared to the majority of task participants that do utilize WN tags . Our method can produce labeled pattern clusters , which can be potentially useful for automatic discovery of additional instances for a given relationship . We intend to pursue this promising direction in future work .

Challenge: Using annotated data or semantic resources such as WordNet for relation classification introduces errors and such data is not available in many domains and languages.
Approach: They propose an unsupervised pattern clustering method for nominal relation classification using a large generic corpus enabling scale in domain and language.
Outcome: Experiments on the ACL SemEval-07 dataset show the proposed method performs better than existing methods that do not use disambiguation tags.