Event extraction is a challenging problem in information extraction, designed to extract structured information from unstructured text. The existing event extraction methods are mostly based on the pipeline model and ...
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Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evide...
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With the development of IOT and 5G technology, people’s demand for information acquisition is more inclined to accuracy, intelligence and timeliness. How to help designer obtain the real-time information of specific ...
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With the development of IOT and 5G technology, people’s demand for information acquisition is more inclined to accuracy, intelligence and timeliness. How to help designer obtain the real-time information of specific product reviews from the massive online consumers and upgrade the new design strategy has become a hot topic for research. In this paper, we define the problem as an aspect extraction task, and propose a novel deep learning model that comprises of three modules: pre-training language model embedding, multi-scale transmission network and contextual summary, which aims to provide an end-to-end solution without any additional supervision. To this end, we adopt BERT to overcome the disadvantage of traditional embedding methods, which cannot combine contextual information. Multi-scale transmission network is proposed to integrate the Bi-GRU and a group of CNN networks to extract sequential and local features of words respectively. Contextual summary is a tailor-made representation distilled from the input sentence, conditioned on each current word, and thus can assist aspect prediction. Experimental results over three benchmark SemEval datasets clearly illustrate that our model can achieve the state-of-the-art performance.
The rapid growth of scientific papers makes it difficult to query related papers efficiently, accurately and with high coverage. Traditional citation recommendation algorithms rely heavily on the metadata of query doc...
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The rapid growth of scientific papers makes it difficult to query related papers efficiently, accurately and with high coverage. Traditional citation recommendation algorithms rely heavily on the metadata of query documents,which leads to the low quality of recommendation results. In this paper,DeepCite, a content-based hybrid neural network citation recommendation method is proposed. First, the BERT model was used to extract the high-level semantic representation vectors in the text, then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence, and the text vectors were matched in depth to generate candidate sets. Further, the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multisource features. In the reranking stage, a variety of Metapath features were extracted from the citation network, and added to the deep neural network to learn, and the ranking of recommendation results were optimized. Compared with PWFC, ClusCite, BM25, RW, NNRank models, the results of the Deepcite algorithm presented in the ANN datasets show that the precision(P@20), recall rate(R@20), MRR and MAP indexesrise by 2.3%, 3.9%, 2.4% and 2.1%respectively. Experimental results on DBLP datasets show that the improvement is 2.4%, 4.3%, 1.8% and 1.2% respectively. Therefore, the algorithm proposed in this paper effectively improves the quality of citation recommendation.
Traditional prediction methods of legal judgment rarely work well on few-shot charge prediction task, which is intended to predict possible crimes, laws and terms according to a given few-shot case description. A majo...
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Traditional prediction methods of legal judgment rarely work well on few-shot charge prediction task, which is intended to predict possible crimes, laws and terms according to a given few-shot case description. A major challenge in developing an efficient few-shot charge prediction algorithm is how to effectively use all available features contained in the typical cases. To this end, we proposed a novel few-shot charge prediction method based on deep learning framework, named Semi-supervised Learning with BERT-text-Convolutional neural network (i.e. SLBCNN), which utilizes the Attribute-Tri-Training method to extract the features from the few-shot case data. Experiments on three datasets of Criminal-S, Criminal-M, and Criminal-L demonstrate that SLBCNN achieves the state-of-the-art performance, boosting the F1 by 5.2%, 4.2%, and 1.5%, respectively.
Event extraction is a challenging problem in information extraction, designed to extract structured information from unstructured text. The existing event extraction methods are mostly based on the pipeline model and ...
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Event extraction is a challenging problem in information extraction, designed to extract structured information from unstructured text. The existing event extraction methods are mostly based on the pipeline model and use the traditional language representation model for word embedding. It is impossible to model the polysemy and the features is very simple, besides, the pipeline model is also easy to generate cascading errors. Aiming at the above problems, we propose a joint event extraction model, which combines part-of-speech features, entity features and word representation of BERT model. Because of the BERT can’t capture local features, we use multi-CNN to capture the local features of different scales. and further use BiLSTM to capture context features to assist joint event extraction. Experimental results show that the precision, recall and F 1 of the method on the CEC dataset are 88.83%, 90.91% and 89.86%, respectively, which are 1.61%, 3.6% and 2.6% higher than the PLMEE model.
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