dc.contributor.author | Sun, Mengtao | |
dc.contributor.author | Yang, Qiang | |
dc.contributor.author | Wang, Hao | |
dc.contributor.author | Pasquine, Mark | |
dc.contributor.author | Hameed, Ibrahim A. | |
dc.date.accessioned | 2022-12-01T08:05:10Z | |
dc.date.available | 2022-12-01T08:05:10Z | |
dc.date.created | 2022-01-23T09:19:39Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Information 13, no. 2: 49. | en_US |
dc.identifier.issn | 2078-2489 | |
dc.identifier.uri | https://hdl.handle.net/11250/3035172 | |
dc.description.abstract | In some languages, Named Entity Recognition (NER) is severely hindered by complex linguistic structures, such as inflection, that will confuse the data-driven models when perceiving the word’s actual meaning. This work tries to alleviate these problems by introducing a novel neural network based on morphological and syntactic grammars. The experiments were performed in four Nordic languages, which have many grammar rules. The model was named the NorG network (Nor: Nordic Languages, G: Grammar). In addition to learning from the text content, the NorG network also learns from the word writing form, the POS tag, and dependency. The proposed neural network consists of a bidirectional Long Short-Term Memory (Bi-LSTM) layer to capture word-level grammars, while a bidirectional Graph Attention (Bi-GAT) layer is used to capture sentence-level grammars. Experimental results from four languages show that the grammar-assisted network significantly improves the results against baselines. We also investigate how the NorG network works on each grammar component by some exploratory experiments. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Learning the Morphological and Syntactic Grammars for Named Entity Recognition | en_US |
dc.title.alternative | Learning the Morphological and Syntactic Grammars for Named Entity Recognition | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.volume | 13 | en_US |
dc.source.journal | Information | en_US |
dc.source.issue | 2 | en_US |
dc.identifier.doi | 10.3390/info13020049 | |
dc.identifier.cristin | 1987992 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |