SpaDeLeF: A Dataset for Hierarchical Classification of Lexical Functions
  for Collocations in Spanish
Yevhen Kostiuk Grigori Sidorov Olga Kolesnikova
In natural language processing (NLP), lexical function is a concept to
unambiguously represent semantic and syntactic features of words and phrases in
text first crafted in the Meaning-Text Theory. Hierarchical classification of
lexical functions involves organizing these features into a tree-like hierarchy
of categories or labels. This is a challenging task as it requires a good
understanding of the context and the relationships among words and phrases in
text. It also needs large amounts of labeled data to train language models
effectively. In this paper, we present a dataset of most frequent Spanish
verb-noun collocations and sentences where they occur, each collocation is
assigned to one of 37 lexical functions defined as classes for a hierarchical
classification task. Each class represents a relation between the noun and the
verb in a collocation involving their semantic and syntactic features. We
combine the classes in a tree-based structure, and introduce classification
objectives for each level of the structure. The dataset was created by
dependency tree parsing and matching of the phrases in Spanish news. We provide
baselines and data splits for each objective.