Statistical language models within the algebra of weighted rational languages

Statistical language models are an important tool in natural language processing. They represent prior knowledge about a certain language which is usually gained from a set of samples called a corpus. In this paper, we present a novel way of creating N-gram language models using weighted finite auto...

Teljes leírás

Elmentve itt :
Bibliográfiai részletek
Szerzők: Hanneforth Thomas
Würzner Kay-Michael
Testületi szerző: Weighted Automata : Theory and Applications (2008) (Dresden)
Dokumentumtípus: Cikk
Megjelent: 2009
Sorozat:Acta cybernetica 19 No. 2
Kulcsszavak:Számítástechnika, Kibernetika
Tárgyszavak:
Online Access:http://acta.bibl.u-szeged.hu/12868
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520 3 |a Statistical language models are an important tool in natural language processing. They represent prior knowledge about a certain language which is usually gained from a set of samples called a corpus. In this paper, we present a novel way of creating N-gram language models using weighted finite automata. The construction of these models is formalised within the algebra underlying weighted finite automata and expressed in terms of weighted rational languages and transductions. Besides the algebra we make use of five special constant weighted transductions which rely only on the alphabet and the model parameter N. In addition, we discuss efficient implementations of these transductions in terms of virtual constructions. 
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