# Graph-Based Taxonomic Semantic Class Labeling
## 1 Introduction  
Taxonomic semantic class labeling is a procedure for extracting a common semantic label for a set of conceptually related lexemes. The automation of taxonomic labeling can be exploited in many applications, as it can represent tacit knowledge and infer ontological relations, which is important for various subsequent natural language processing (NLP) tasks and applications [1]. In lexicology, taxonomic semantic labels enable better standardization of lexical descriptions and allow the construction of \"pseudo-definitions\"--compact descriptions of the lexical conceptual content. However, the automatic creation of taxonomic labels for a lexeme or a set of lexemes is a non-trivial task with multi-class classification and multi-label classification procedures. One of the major multi-class classification problems of this task is related to the polysemous structure of a lexeme. Namely, an ambiguous word can have multiple senses or acquire new meanings that are often not even semantically related. For instance, the noun _bass_ can be labeled as a species of fish or as an instrument. Moreover, for a set of lexemes representing a semantic class, taxonomic labeling derives a target semantic class label as a result of the categorial relation _is_a_ shared with all semantic class members [2]. For instance, the set of source lexemes _banana_, _apple_, _mango_, and _pear_ could be labeled as _fruit_ or _plants_, while the community of lexemes _banana_, _apple_, _cookie_, _icceream_, and _sandwich_ should be labeled as _snack_. Taxonomic semantic class labeling, in this sense, is a kind of generalization of a semantic field determined by the semantic scope of associated lexical units using a more abstract term that can taxonomically represent its semantic class. From the perspective of cognitive linguistics [3] and cognitive grammar [4], these two lexical phenomena, word disambiguation and taxonomic labeling, can be seen as mutuallysupportive tasks. The meaning of a word is always profiled in relation to other words through a process of semantic framing and syntactic-semantic constant [3] (Chapter 2). This means that conceptually similar words tend to form a radial network [3] (Chapter 3), invoked by a certain semantic frame [3] (Chapter 10). Using this theoretical background, we can assume that a source lexeme can be disambiguated by identifying the associated lexical networks that form a semantic field [5]. We can hypothesize that the clustered lexemes can be utilized to label related prototypical semantic frames as categorizations. Additionally, we can suppose that these semantic processes employ particular syntactic constructions. The Construction Grammar Conceptual Network (ConGraCNet) [5] semantic class labeling method proposed in this paper introduces the procedure to identify clustered lexical fields of a source lexeme node as a semantic class and reveal their taxonomic labels. It is based on the use of syntactic dependencies to analyze information structures in a text. The use of syntactic dependencies is an established approach that has been shown to be effective in a wide range of applications. Syntactic dependencies refer to the relationships between words in a sentence, such as the subject-verb relationship or the direct object-verb relationship. These relationships can be represented using a graph, where each word is represented as a node and the dependencies between words are represented as edges between nodes. One of the main advantages of using syntactic dependencies is that they provide a structured representation of the conceptual relationships between words in a sentence, which can be used to extract valuable information and make inferences about the meaning of the sentence. This is particularly useful in natural language processing tasks such as summarization and information extraction, which involve analyzing the structure of a sentence and identifying the main subjects and objects in a sentence, the manner, time, aspect of the process, etc.