Work

Automated WordNet Construction Using Word Embeddings

Year: 2017

Type: conference-paper

Abstract: We present a fully unsupervised method for automated construction of WordNets based upon recent advances in distributional representations of sentences and word-senses combined with readily available machine translation tools.The approach requires very few linguistic resources and is thus extensible to multiple target languages.To evaluate our method we construct two 600-word test sets for word-to-synset matching in French and Russian using native speakers and evaluate the performance of our method along with several other recent approaches.Our method exceeds the best language-specific and multi-lingual automated WordNets in F-score for both languages.The databases we construct for French and Russian, both languages without large publicly available manually constructed WordNets, will be publicly released along with the test sets.

DOI: 10.18653/v1/w17-1902

Authors: Mikhail Khodak, Andrej Risteski, Christiane Fellbaum, Sanjeev Arora

Institution: Princeton University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: National Science Foundation, Office of Naval Research

Awards: AF: Medium: Towards Provable Bounds for Machine Learning, AF: Small: Linear Algebra++ and applications to machine learning, G4353487559, G8876996369

OpenAlex ID: W2740287278