@inproceedings{guenoune-lafourcade-2024-symbolic,
    title = "Symbolic Learning of Rules for Semantic Relation Types Identification in {F}rench Genitive Postnominal Prepositional Phrases",
    author = "Guenoune, Hani  and
      Lafourcade, Mathieu",
    editor = "Zock, Michael  and
      Chersoni, Emmanuele  and
      Hsu, Yu-Yin  and
      de Deyne, Simon",
    booktitle = "Proceedings of the Workshop on Cognitive Aspects of the Lexicon @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.cogalex-1.4",
    pages = "32--41",
    abstract = "We are interested in the semantic relations conveyed by polylexical entities in the postnominal prepositional noun phrases form {``}A de B{''} (A of B). After identifying a relevant set of semantic relations types, we proceed, using generative AI, to build a collection of phrases, for each semantic relation type identified. We propose an algorithm for creating rules that allow the selection of the relation between A and B in noun phrases of each type. These rules correspond to selecting from a knowledge base the appropriate neighborhood of a given term. For the phrase {``}d{\'e}sert d{'}Alg{\'e}rie{''} carrying the location relation, the term {``}d{\'e}sert{''} is identified as a geographical location, and {``}Alg{\'e}rie{''} as a country. These constraints are used to automatically learn a set of rules for selecting the location relation for this type of example. Rules are not exclusive as there may be instances that fall under multiple relations. In the phrase {``}portrait de sa m{\`e}re - the portrait of his/her mother{''}, all of depiction, possession, and producer types are a possible match.",
}

We are interested in the semantic relations conveyed by polylexical entities in the postnominal prepositional noun phrases form {``}A de B{''} (A of B). After identifying a relevant set of semantic relations types, we proceed, using generative AI, to build a collection of phrases, for each semantic relation type identified. We propose an algorithm for creating rules that allow the selection of the relation between A and B in noun phrases of each type. These rules correspond to selecting from a knowledge base the appropriate neighborhood of a given term. For the phrase {``}d{\'e}sert d{'}Alg{\'e}rie{''} carrying the location relation, the term {``}d{\'e}sert{''} is identified as a geographical location, and {``}Alg{\'e}rie{''} as a country. These constraints are used to automatically learn a set of rules for selecting the location relation for this type of example. Rules are not exclusive as there may be instances that fall under multiple relations. In the phrase {``}portrait de sa m{\`e}re - the portrait of his/her mother{''}, all of depiction, possession, and producer types are a possible match.
Guenoune, Hani Lafourcade, Mathieu
Symbolic Learning of Rules for Semantic Relation Types Identification in French Genitive Postnominal Prepositional Phrases