In recent years, prediction-based distributional word vectors (i.e., word embeddings) have become ubiquitous in natural language processing. While word embeddings robustly capture existence of semantic associations between words, they fail to reflect – due to the distributional nature of embedding models – the exact type of the semantic link that holds between the words, that is, the exact semantic relation (e.g., synonymy, antonymy, hypernymy). This talk presents an overview of recent models that fine-tune distributional word spaces for specific lexico-semantic relations, using external knowledge from lexico-semantic resources (e.g., WordNet) for supervision. It analyzes models for specializing embeddings for semantic similarity (vs. other types of semantic association) as well as models that specialize word vectors for detecting particular relations, both symmetric (e.g., synonymy, antonymy) and asymmetric (e.g., hypernymy, meronymy). The talk will also examine evaluation procedures and downstream tasks that benefit from specializing embedding spaces. Finally, it will demonstrate how to transfer embedding specializations to resource-lean languages, for which no external lexico-semantic resources exist.
Goran Glavaš is an Assistant Professor for Natural Language Processing at the Data and Web Science group, School of Business Informatics and Mathematics, University of Mannheim. He obtained his Ph.D. at the Text Analysis and Knowledge Engineering Lab (TakeLab), Faculty of Electrical Engineering and Computing, University of Zagreb. His research efforts and interests are in the areas of statistical natural language processing (NLP) and information retrieval (IR), with foci on lexical and computational semantics, multi-lingual and cross-lingual NLP and IR, information extraction, and NLP applications for social sciences. Goran has (co-)authored over 50 publications in the areas of NLP and IR, publishing at top-tier NLP and IR venues (ACL, EMNLP, EACL, SIGIR). He is a co-organizer of the TextGraphs workshop series on Graph-Based NLP and has served as a program committee member / reviewer for renowned journals (Computational Linguistics, Artificial Intelligence, Natural Language Engineering, Information Retrieval Journal) and conferences (ACL, EMNLP, AAAI, IJCAI, SIGIR) in the field. He is a member of the Association for Computational Linguistics.