Selectional Preference (SP) captures the tendency of a word to semantically select other words to be in direct syntactic relation with it, and thus informs us about syntactic word configurations that are meaningful. Therefore SP is a valuable resource for Natural Language Processing (NLP) systems and for semanticists. Learning SP has generally been seen as a supervised task, because it requires a parsed corpus as a source of syntactically related word pairs. In this paper we show that simple distributional analysis can learn a good amount of SP without the need for an annotated corpus. We extend the general word embedding technique with directional word context windows giving word representations that better capture syntagmatic relations. We test on the SP-10K dataset and demonstrate that syntagmatic embeddings outperform the paradigmatic embeddings. We also evaluate supervised version of these embeddings and show that unsupervised syntagmatic embeddings can be as good as supervised embeddings. We also make available the source code of our implementation 1 .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.