# Graph-Based Taxonomic Semantic Class Labeling
## 3 Graph-Based Taxonomic Semantic Associative Community Labeling
### Dependency-Based Taxonomic Semantic Class Labeling  
The taxonomic labeling method described in this subsection does not rely on a commonsense knowledge database as an outside lexical resource. It is based on a corpus dependency-based graph computational linguistic approach. This graph method labels different semantic classes of a source lexeme based on the evaluation of syntactic-semantic collocations of the _is_a_ relation in a corpus.  
The idea behind our approach is that the conceptual taxonomic structure, i.e., the paradigmatic lexical relation hypernym/hyponym, is expressed in the linguistic usage by nominal predicates and copula structures, for example \"car is a vehicle\" or \"bass is a fish\". Conversely, we can explore unsupervised graph-based algorithms to extract the taxonomic hyponym-hypernym candidates from the syntactically tagged corpus analysis of a _is_a_ syntactic dependency.  
Taxonomic semantic label candidates for a community of associated lexemes are selected based on syntactic-semantic _is_a_ dependency relations extracted from a syntactically tagged corpus. The highest-ranked lexical collocates in the community are prototypical taxonomic representatives of the semantic information people use to describe, differentiate, and reason about these associated concepts.  
The syntactic dependency _is_a_ typically establishes a semantic association between two lexemes, one being a taxonomic subclass of the other. This type of association has a categorical semantic function. However, _is_a_ is also used in metaphorical expressions, such as \"Love is a fire that consumes lovers\", \"Reading is a way to spend the time\", or \"She is an object of desire\", where the target argument metaphorically maps the content of the conceptual domain onto the source domain. This metaphorical type of association produces noise in the taxonomic labeling task. The proposed procedure to extract the metaphorical from categorical taxonomic collocates uses the fact that the source and target domain in the metaphorical mapping have larger ontological differences than the categorical type of association [51]. In this way, we propose an algorithm that not only identifies the taxonomic labels for the source lexeme, but also serves to filter the lexemes used for the prototypical metaphorical mappings.  
The _is_a_ relation can be expressed by a copula relation, which is used to link a subject to a nonverbal predicate. The copula is often a verb, but nonverbal (pronominal) copulas  
Figure 1: Fo\([and|or]_{ethics}\): ConGraCNet graph representation of the sense structure of a source noun lexeme _ethics_ in enTenTen13.  
are also common in many languages of the world. In this study, we rely on a set of predetermined syntactic patterns that use the _is_ a syntactic-semantic lexical relations defined by the Word Sketch grammars, classified as \"w% is a...\" and \"...is a w%\" [52].  
The data for the construction of the _is_ a network represented in this article are obtained from the English Web corpora [53] with preprocessed \"[word] _is_a_\" dependency.  
The extraction of the _is_a_ collocates is performed in both directions to capture the widest array of taxonomic relations, such as the relation of _banana-fruit_ in the examples \"A banana is a fruit\" and \"This fruit is a banana\". That is, for a given lexemex we extract its [lexemex is_a lexemex] and its [lexemex by_a lexemex] collocates from the corpus. The construction of the directed lexical network based on the _is_ a collocations allows us to formalize a syntactically based categorical labeling method that takes into account a broader range of semantic features of the _is_a_ patterns, including the hypernym and hyponym aspects of the relations.