Convegno From Cognitive Linguistics to Language Engineering, and Back

In the past decade, advancements in deep learning, particularly in the field of natural language processing (NLP) and text mining, have significantly enhanced semantic analysis tasks such as text classification, word sense disambiguation, machine translation, text summarization, question answering, and sentiment analysis. This progress is largely attributed to the concept of word embedding, a word’s meaning representation obtained through numeric coordinates, also known as vectors. Current word embeddings, derived from large textual corpora, have demonstrated efficacy but raise questions about their alignment with human language processing. The WEMB project aims to address this by pursuing two objectives: firstly, gaining a deeper understanding of how word embeddings align with human language processing, and secondly, leveraging this understanding to develop a new generation of embeddings for NLP tasks.

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