Name
Abstract | ||
|---|---|---|
In this paper, we present a stochastic language model using dependency. This model considers a sentence as a word sequence and predicts each word from left to right. The history at each step of prediction is a sequence of partial parse trees covering the preceding words. First our model predicts the partial parse trees which have a dependency relation with the next word among them and then predicts the next word from only the trees which have a dependency relation with the next word. Our model is a generative stochastic model, thus this can be used not only as a parser but also as a language model of a speech recognizer. In our experiment, we prepared about 1,000 syntactically annotated Japanese sentences extracted from a financial newspaper and estimated the parameters of our model. We built a parser based on our model and tested it on approximately 100 sentences of the same newspaper. The accuracy of the dependency relation was 89.9%, the highest accuracy level obtained by Japanese stochastic parsers. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.3115/990820.990901 | COLING |
Keywords | Field | DocType |
generative stochastic model,structural word prediction model,financial newspaper,language model,preceding word,dependency relation,partial parse tree,next word,japanese stochastic parsers,stochastic language model,word sequence,stochastic model | Dependency relation,Factored language model,Computer science,Speech recognition,Natural language processing,Artificial intelligence,Stochastic modelling,Generative grammar,Parsing,Sentence,Language model | Conference |
Volume | ISBN | Citations |
C00-1 | 1-55860-717-X | 6 |
PageRank | References | Authors |
0.52 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shinsuke Mori | 1 | 474 | 47.78 |
| Masafumi Nishimura | 2 | 112 | 22.77 |
| Nobuyasu Itoh | 3 | 65 | 13.19 |
| Shiho Ogino | 4 | 44 | 7.44 |
| Hideo Watanabe | 5 | 88 | 9.54 |