There are many possible different semantic relationships between nominals.

Each of the extracted clusters corresponds to some unspecified semantic relationship .

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 nonnnabl.Forexanqfle,ninouncxnnpoundsrnany different semantic relationships are encoded by the same simple form (Girju et al., 2005): ‘dog food’ denotes food consumed by dogs, while ‘summer mom-

The semantic relationships between the components of noun compounds and between nominals in general are not easy to categorize rigorously.

Our pattern clustering algorithm is designed for the unsupervised definition and discovery of generic semantic relationships .

Numerous methods have been devised for classification of semantic relationships , among which those holding between nominals constitute a prominent category.

Since (Hearst, 1992), numerous works have used patterns for discovery and identification of instances of semantic relationships (e.g., (Girju et al., 2006; Snow et al., 2006; Banko et al, 2007)).

We present a novel framework for the discovery and representation of general semantic relationships that hold between lexical items.

Each such cluster is set of patterns that can be used to identify, classify or capture new instances of some unspecified semantic relationship .

They aim to find relationship instances rather than identify generic semantic relationships .

As discussed in Section 2, the evaluation of semantic relationship structures is nontrivial.

The first is the quality (precisiorflrecall) of individual pattern clusters: does each pattern cluster capture lexical item pairs of the same semantic relationship ?

does it recognize many pairs of the same semantic relationship ?

We present a simple linguistically-motivated method for characterizing the semantic relations that hold between two nouns.

Given a pair of nouns, we try to characterize the semantic relation between them by leveraging the vast size of the Web to build linguistically-motivated lexically-specific features.

2.1 Characterizing Semantic Relations

Turney (2006a) presents an unsupervised algorithm for mining the Web for patterns expressing implicit semantic relations .

They test their system against both Lauer’s 8 prepositional paraphrases and another set of 21 semantic relations , achieving up to 54% accuracy on the latter.

We further experimented with the SemEval’07 task 4 dataset (Girju et al., 2007), where each example consists of a sentence, a target semantic relation , two nominals to be judged on whether they are in that relation, manually annotated WordNet senses, and the Web query used to obtain the sentence:

We compare the results for the 1911 and 1987 Roget’s Thesauri with a variety of WordNet-based semantic relatedness measures — see Table 5.

Other methods of determining sentence semantic relatedness expand term relatedness functions to

We ran the well-established tasks of determining semantic relatedness of pairs of terms and identifying synonyms (J armasz and Szpakowicz, 2004).

They propose a method of determining semantic relatedness between pairs of terms.

Similar experiments were carried out using WordNet in combination with a variety of semantic relatedness functions.

airport to aeropue rt 0 s), 30 were semantically related (e.g.

Of the true errors, the most common arose from semantically related words which had strong context feature correlations (see table 4(b)).

Here, the broad trend is for words which are either translations or semantically related across languages to be close in canonical space.