Name
Abstract | ||
|---|---|---|
Almost all the state-of-the-art methods for Character-based Chinese dependency parsing ignore the complete dependency subtree information built during the parsing process, which is crucial for parsing the rest part of the sentence. In this paper, we introduce a novel neural network architecture to capture dependency subtree feature. We extend and improve recent works in neural joint model for Chinese word segmentation, POS tagging and dependency parsing, and adopt bidirectional LSTM to learn n-gram feature representation and context information. The neural network and bidirectional LSTMs are trained jointly with the parser objective, resulting in very effective feature extractors for parsing. Finally, we conduct experiments on Penn Chinese Treebank 5, and demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser. The results show that our model outperforms the state-of-the-art neural joint models in Chinese word segmentation, POS tagging and dependency parsing. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-99501-4_17 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Chinese word segmentation,POS tagging and dependency parsing,Dependency subtree,Neural network architecture | Computer science,Tree (data structure),Neural network architecture,Dependency grammar,Text segmentation,Natural language processing,Artificial intelligence,Treebank,Parsing,Artificial neural network,Sentence | Conference |
Volume | ISSN | Citations |
11109 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hang Liu | 1 | 835 | 90.79 |
| Mingtong Liu | 2 | 2 | 2.43 |
| Yujie Zhang | 3 | 251 | 52.63 |
| Jin An Xu | 4 | 15 | 24.50 |
| Yufeng Chen | 5 | 38 | 16.55 |