Modeling semantic compositionality of relational patterns

AbstractVector representation is a common approach for expressing the meaning of a relational pattern. Most previous work obtained a vector of a relational pattern based on the distribution of its context words (e.g., arguments of the relational pattern), regarding the pattern as a single ‘word’. However, this approach suffers from the data sparseness problem, because relational patterns are productive, i.e., produced by combinations of words. To address this problem, we propose a novel method for computing the meaning of a relational pattern based on the semantic compositionality of constituent words. We extend the Skip-gram model (Mikolov et al., 2013) to handle semantic compositions of relational patterns using recursive neural networks. The experimental results show the superiority of the proposed method for modeling the meanings of relational patterns, and demonstrate the contribution of this work to the task of relation extraction

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

自然言語の分散表現へのエンコードに関する研究

Tohoku University乾健太郎課

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges