This paper addresses the task of supervised hypernymy detection in Spanish through an order embedding and using pretrained word vectors as input. Although the task has been widely addressed in English, there is not much work in Spanish, and according to our knowledge there is not any available dataset for supervised hypernymy detection in Spanish. We built a supervised hypernymy dataset for Spanish from WordNet and corpus statistics information, with different versions according to the lexical intersection between its partitions: random and lexical split. We show the results of using the resulting dataset within an order embedding consuming pretrained word vectors as input. We show the ability of pretrained word vectors to transfer learning to unseen lexical units according to the results in the lexical split dataset. To finish, we study the results of giving additional information in training time, such as, cohyponym links and instances extracted through patterns.

This paper presents the development of a deep parser for Spanish that uses a HPSG grammar and returns trees that contain both syntactic and semantic information. The parsing process uses a top-down approach implemented using LSTM neural networks, and achieves good performance results in terms of syntactic constituency and dependency metrics, and also SRL. We describe the grammar, corpus and implementation of the parser. Our process outperforms a CKY baseline and other Spanish parsers in terms of global metrics and also for some specific Spanish phenomena, such as clitics reduplication and relative referents.