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 .
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.
They propose an unsupervised pattern clustering method for nominal relation classification using a large generic corpus enabling scale in domain and language.
Experiments on the ACL SemEval-07 dataset show the proposed method performs better than existing methods that do not use disambiguation tags.