SLAOENN:A Deep Network with Structure Learning for Aspect and Opining coExtraction for NLP The task of coextracting aspects and opinion terms is intended to explicitly extract aspect terms that describe entity features and opinion terms that express emotions from usergenerated text. An effective way to accomplish this task is to exploit the relationship between aspect terms and opinion terms by parsing the syntax structure of each sentence. However, this method requires a lot of effort to parse and is highly dependent on the quality of the parsing results. In this paper, we present a deep learning model called SLAOENN (Structural Learning and Aspect and Opining Extraction Neural Networks). The proposed model provides an endtoend solution and does not require any other language resources for preprocessing. Particularly, we use ONLSTM to generate hidden layer with language structure information which can generate constituency tree unsupervised and we serve it as an auxiliary task for aspect and opinion terms extraction. For aspect terms and opinion terms extract task, we propose different attention mechanism, which can exploit the indirect relationship between aspect term and corresponding opining term to achieve more accurate information extraction. The experimental results of SemEvalu0027s three benchmark datasets in 2014 and 2015 show that our model achieves thestateofart performance compared to several baselines.

Attend, Copy, Parse Endtoend Information Extraction from Documents Document information extraction tasks performed by humans create data consisting of a PDF or document image input, and extracted string outputs. This endtoend data is naturally consumed and produced when performing the task because it is valuable in and of itself. It is naturally available, at no additional cost. Unfortunately, stateoftheart word classification methods for information extraction cannot use this data, instead requiring wordlevel labels which are expensive to create and consequently not available for many real life tasks. In this paper we propose the Attend, Copy, Parse architecture, a deep neural network model that can be trained directly on endtoend data, bypassing the need for wordlevel labels. We evaluate the proposed architecture on a large diverse set of invoices, and outperform a stateoftheart production system based on word classification. We believe our proposed architecture can be used on many real life information extraction tasks where word classification cannot be used due to a lack of the required wordlevel labels.