- Tytuł:
- Hilberg’s Conjecture – a Challenge for Machine Learning
- Autorzy:
- Dębowski, Łukasz
- Powiązania:
- https://bibliotekanauki.pl/articles/1373619.pdf
- Data publikacji:
- 2014
- Wydawca:
- Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
- Tematy:
-
statistical language modeling
Hilberg’s conjecture
maximal repetition
grammar-based codes
Santa Fe processes - Opis:
- We review three mathematical developments linked with Hilberg’s conjecture – a hypothesis about the power-law growth of entropy of texts in natural language, which sets up a challenge for machine learning. First, considerations concerning maximal repetition indicate that universal codes such as the Lempel-Ziv code may fail to efficiently compress sources that satisfy Hilberg’s conjecture. Second, Hilberg’s conjecture implies the empirically observed power-law growth of vocabulary in texts. Third, Hilberg’s conjecture can be explained by a hypothesis that texts describe consistently an infinite random object.
- Źródło:
-
Schedae Informaticae; 2014, 23; 33-44
0860-0295
2083-8476 - Pojawia się w:
- Schedae Informaticae
- Dostawca treści:
- Biblioteka Nauki
Artykuł