Semantic Scholar
Open Access
2014
1509 sitasi
Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors
Marco Baroni
Georgiana Dinu
Germán Kruszewski
Abstrak
Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, we perform such an extensive evaluation, on a wide range of lexical semantics tasks and across many parameter settings. The results, to our own surprise, show that the buzz is fully justified, as the context-predicting models obtain a thorough and resounding victory against their count-based counterparts.
Topik & Kata Kunci
Penulis (3)
M
Marco Baroni
G
Georgiana Dinu
G
Germán Kruszewski
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2014
- Bahasa
- en
- Total Sitasi
- 1509×
- Sumber Database
- Semantic Scholar
- DOI
- 10.3115/v1/P14-1023
- Akses
- Open Access ✓