TY - CHAP
T1 - The unsupervised approach
T2 - Grammar induction
AU - Gambino, Omar J.
AU - Calvo, Hiram
N1 - Publisher Copyright:
© Springer International Publishing AG 2018.
PY - 2018
Y1 - 2018
N2 - There are mainly two approaches for creating syntactic dependency analyzers: supervised and unsupervised. The main goal of the first approach is to attain the best possible performance for a single language. For this purpose, a large collection of resources is gathered (using manually annotated corpora with part-of-speech annotations and syntactic and structure tags), which requires a significant amount of work and time. The state of the art in this approach attains syntactic annotation in about 85% of all full sentences (Rooth in Proceedings of the symposium on representation and acquisition of lexical knowledge. AAAI, 1995 [172]); in English, it attains over 90%. On the other hand, the unsupervised approach tries to discover the structure of a text using only raw text, which allows the creation of a dependency analyzer for virtually any language. Here, we explore this second approach. We present the model of an unsupervised dependency analyzer, named DILUCT-GI (GI short for grammar inference).
AB - There are mainly two approaches for creating syntactic dependency analyzers: supervised and unsupervised. The main goal of the first approach is to attain the best possible performance for a single language. For this purpose, a large collection of resources is gathered (using manually annotated corpora with part-of-speech annotations and syntactic and structure tags), which requires a significant amount of work and time. The state of the art in this approach attains syntactic annotation in about 85% of all full sentences (Rooth in Proceedings of the symposium on representation and acquisition of lexical knowledge. AAAI, 1995 [172]); in English, it attains over 90%. On the other hand, the unsupervised approach tries to discover the structure of a text using only raw text, which allows the creation of a dependency analyzer for virtually any language. Here, we explore this second approach. We present the model of an unsupervised dependency analyzer, named DILUCT-GI (GI short for grammar inference).
UR - http://www.scopus.com/inward/record.url?scp=85042874836&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-74054-6_8
DO - 10.1007/978-3-319-74054-6_8
M3 - Capítulo
AN - SCOPUS:85042874836
T3 - Studies in Computational Intelligence
SP - 111
EP - 124
BT - Studies in Computational Intelligence
PB - Springer Verlag
ER -