Generalizing Representations of Lexical Semantic Relations

We propose a new method for unsupervised learning of embeddings for lexical relations in word pairs. The model is trained on predicting the contexts in which a word pair appears together in corpora, then generalized to account for new and unseen word pairs. This allows us to overcome the data sparsity issues inherent in existing relation embedding learning setups without the need to go back to the corpora to collect additional data for new pairs.Proponiamo un nuovo metodo per l’apprendimento non supervisionato delle rappresentazioni delle relazioni lessicali fra coppie di parole (word pair embeddings). Il modello viene allenato a prevedere i contesti in cui compare uns coppia di parole, e successivamente viene generalizzato a coppie di parole nuove o non attestate. Questo ci consente di superare i problemi dovuti alla scarsità di dati tipica dei sistemi di apprendimento di rappresentazioni, senza la necessità di tornare ai corpora per raccogliere dati per nuove coppie di parole

Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources

In this work, we present an effective method for semantic specialization of word vector representations. To this end, we use traditional word embeddings and apply specialization methods to better capture semantic relations between words. In our approach, we leverage external knowledge from rich lexical resources such as BabelNet. We also show that our proposed post-specialization method based on an adversarial neural network with the Wasserstein distance allows to gain improvements over state-of-the-art methods on two tasks: word similarity and dialog state tracking.Comment: Accepted to ACL 2020 SR

Ontology-Aware Token Embeddings for Prepositional Phrase Attachment

Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase(PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.Comment: ACL 201