| | |
| | |
Stat |
Members: 3643 Articles: 2'487'895 Articles rated: 2609
28 March 2024 |
|
| | | |
|
Article overview
| |
|
Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures | Lifu Huang
; Avirup Sil
; Heng Ji
; Radu Florian
; | Date: |
4 Jul 2017 | Abstract: | Slot Filling (SF) aims to extract the values of certain types of attributes
(or slots, such as person:cities\_of\_residence) for a given entity from a
large collection of source documents. In this paper we propose an effective DNN
architecture for SF with the following new strategies: (1). Take a regularized
dependency graph instead of a raw sentence as input to DNN, to compress the
wide contexts between query and candidate filler; (2). Incorporate two
attention mechanisms: local attention learned from query and candidate filler,
and global attention learned from external knowledge bases, to guide the model
to better select indicative contexts to determine slot type. Experiments show
that this framework outperforms state-of-the-art on both relation extraction
(16\% absolute F-score gain) and slot filling validation for each individual
system (up to 8.5\% absolute F-score gain). | Source: | arXiv, 1707.1075 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser claudebot
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |