2017/09/12 by Ledell Wu, Wu, Ledell, Adam Fisch +9 · 2 voices · 19 citations
Computer Science · #Advanced Graph Neural Networks #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1709.03856
openalex publication_date 2017/09/12 · arxiv published 2017/09/12 · arxiv created 2017/11/21 · arxiv updated 2017/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and learning word, sentence or document level embeddings. In each case the model works by embedding those entities comprised of discrete features and comparing them against each other -- learning similarities dependent on the task. Empirical results on a number of tasks show that StarSpace is highly competitive with existing methods, whilst also being generally applicable to new cases where those methods are not.