AutoExtend
Combining Word Embeddings with Semantic Resources
Bibliographic Data
| ID | 12155697 |
|---|---|
| Authors | Sascha Rothe (Ludwig-Maximilians-Universität München, corresponding author), Hinrich Schütze (Ludwig-Maximilians-Universität München) |
| Year | 2017 |
| Volume | 43 |
| Issue | 3 |
| Pages | 593-617 |
| Publication date | 2017-06-09 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Computational Linguistics (JOURNAL) |
| Journal identifiers | ISSN: 0891-2017 • E-ISSN: 1530-9312 |
| Publisher | Association for Computational Linguistics (PUBLISHER • US) |
| DOI | 10.1162/coli_a_00294 |
| OpenAlex | W2622670270 |
| Language | EN |
| Citations received | 1 |
| References cited | 40 |
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
Context (archaeology · Encoding (memory · Image (mathematics · Linguistics · Natural language processing · Semantic similarity · Similarity (geometry · Word (group theory · WordNet · Computer Science · Multimodal Machine Learning Applications · Natural Language Processing Techniques · Topic Modeling · Artificial Intelligence
| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |