Modeling texts with networks: comparing five approaches to sentence representation

Complex networks offer a powerful framework for modeling linguistic phenomena. This study compares five distinct methods for representing sentences as networks, each with unique edge definitions: (1) a lines approach, where edges represent token (e.g., word) adjacency; (2) a close-range co-occurrence approach, where edges are based on the probability of tokens co-occurring at distance one or two; (3) a cliques approach, where edges connect tokens co-occurring within the same sentence; (4) a dependency-based approach, where edges are defined by syntactic dependencies extracted by a parser; (5) an IF-trimmed-subgraphs approach, where edges are determined by the Incidence-Fidelity (IF) Index. While the first four approaches are well established in the literature, the last one is a novel proposal. We also examined the effects of limiting the vertices to lemmas (i.e., words with inflections removed) and to lexical lemmas (i.e., nouns, adjectives, verbs, and adverbs) as opposed to the unaltered words. Our results reveal that these approaches yield networks with varying average minimal path lengths and degrees, influencing the interpretation of results. While small-world behavior remains consistent across networks, scale-free behavior analysis is affected. Notably, excluding functional words significantly alters degree distributions. We suggest, in order of relevance and according to the resources available, the dependency-based, the close-range co-occurrence, and the lines approaches for cases in which syntactic relations are central, and the IF-trimmed-subgraphs and the cliques approaches for cases in which semantic relations are central. Graphical Abstract Representation of the sentence “we calculated two sets of adjusted values as follows” using five approaches - (1) the lines approach, (2) the close-range cooccurrence approach, (3) the cliques approach, (4) the dependency-based approach, and (5) the IF-trimmed-subgraphs approach - and three vertex definitions - (1) vertices representing unaltered words, (2) vertices representing lemmas, and (3) vertices representing lexical lemmas