2017/06/09 by Sascha Rothe, Hinrich Schütze · 2 citations
Computer Science · #Topic Modeling #Natural Language Processing Techniques #Multimodal Machine Learning Applications #Computer science #Word (group theory) #WordNet #Natural language processing #Artificial intelligence #Semantic similarity #Context (archaeology) #Encoding (memory) #Similarity (geometry) #Linguistics #Image (mathematics)
paper · pdf · doi:10.1162/coli_a_00294
openalex publication_date 2017/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/15
We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings live in the same vector space as the input word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet, GermaNet, and Freebase as semantic resources. AutoExtend achieves state-of-the-art performance on Word-in-Context Similarity and Word Sense Disambiguation tasks.