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公开(公告)号：US10817668B2
公开(公告)日：2020-10-27
申请号：US16199422
申请日：2018-11-26
Applicant: SAP SE
Inventor： Ruidan He
Abstract: Methods, systems, and computer-readable storage media for receiving a source domain data set including a set of source document and source label pairs, each source label corresponding to a source domain and indicating a sentiment attributed to a respective source document, receiving a target domain data set including a set of target documents absent target labels, processing documents of the source and target domains using a feature encoder of a DAS platform, to map the documents of the source and target domains to a shared feature space through feature representations, the processing including minimizing a distance between the feature representations of the source domain, and feature representations of the target domain based on a set of loss functions, providing an ensemble prediction from the processing, and providing predicted labels based on the ensemble prediction, the predicted labels being used by the sentiment classifier to classify documents from the target domain.
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公开(公告)号：US20200167418A1
公开(公告)日：2020-05-28
申请号：US16199422
申请日：2018-11-26
Applicant: SAP SE
Inventor： Ruidan He
Abstract: Methods, systems, and computer-readable storage media for receiving a source domain data set including a set of source document and source label pairs, each source label corresponding to a source domain and indicating a sentiment attributed to a respective source document, receiving a target domain data set including a set of target documents absent target labels, processing documents of the source and target domains using a feature encoder of a DAS platform, to map the documents of the source and target domains to a shared feature space through feature representations, the processing including minimizing a distance between the feature representations of the source domain, and feature representations of the target domain based on a set of loss functions, providing an ensemble prediction from the processing, and providing predicted labels based on the ensemble prediction, the predicted labels being used by the sentiment classifier to classify documents from the target domain.
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公开(公告)号：US10755174B2
公开(公告)日：2020-08-25
申请号：US15484577
申请日：2017-04-11
Applicant: SAP SE
Inventor： Ruidan He , Daniel Dahlmeier
IPC: G06N3/08 , G06N3/04 , G06F40/30 , G06F40/216 , G06F40/289
Abstract: Methods, systems, and computer-readable storage media for receiving a vocabulary, the vocabulary including text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file, associating each word in the vocabulary with a feature vector, providing a sentence embedding for each sentence of the vocabulary based on a plurality of feature vectors to provide a plurality of sentence embeddings, providing a reconstructed sentence embedding for each sentence embedding based on a weighted parameter matrix to provide a plurality of reconstructed sentence embeddings, and training the unsupervised neural attention model based on the sentence embeddings and the reconstructed sentence embeddings to provide a trained neural attention model, the trained neural attention model being used to automatically determine aspects from the vocabulary.
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公开(公告)号：US10223354B2
公开(公告)日：2019-03-05
申请号：US15478363
申请日：2017-04-04
Applicant: SAP SE
Inventor： Ruidan He , Daniel Dahlmeier
Abstract: Methods, systems, and computer-readable storage media for receiving a vocabulary that includes text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file, providing word embeddings based on the vocabulary, the word embeddings including word vectors for words included in the vocabulary, clustering word embeddings to provide a plurality of clusters, each cluster representing an aspect inferred from the vocabulary, determining a respective association score between each word in the vocabulary and a respective aspect, and providing a word ranking for each aspect based on the respective association scores.
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Patent Agency Ranking
公开(公告)号：US10726207B2
公开(公告)日：2020-07-28
申请号：US16200829
申请日：2018-11-27
Applicant: SAP SE
Inventor： Ruidan He
Abstract: Methods, systems, and computer-readable storage media for receiving a set of document-level training data including a plurality of documents, each document having a sentiment label associated therewith, receiving a set of aspect-level training data including a plurality of aspects, each aspect having a sentiment label associated therewith, training the aspect-level sentiment classifier including a long short-term memory (LSTM) network, and an output layer using one or more of pretraining, and multi-task learning based on the document-level training data and the aspect-level training data, pretraining including initializing parameters based on pretrained weights that are fine-tuned during training, and multi-task learning including simultaneous training of document-level classification and aspect-level classification, and providing the aspect-level sentiment classifier for classifying one or more aspects in one or more sentences of one or more input documents based on sentiment classes.
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公开(公告)号：US20180293499A1
公开(公告)日：2018-10-11
申请号：US15484577
申请日：2017-04-11
Applicant: SAP SE
Inventor： Ruidan He , Daniel Dahlmeier
IPC: G06N3/08
Abstract: Methods, systems, and computer-readable storage media for receiving a vocabulary, the vocabulary including text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file, associating each word in the vocabulary with a feature vector, providing a sentence embedding for each sentence of the vocabulary based on a plurality of feature vectors to provide a plurality of sentence embeddings, providing a reconstructed sentence embedding for each sentence embedding based on a weighted parameter matrix to provide a plurality of reconstructed sentence embeddings, and training the unsupervised neural attention model based on the sentence embeddings and the reconstructed sentence embeddings to provide a trained neural attention model, the trained neural attention model being used to automatically determine aspects from the vocabulary.
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公开(公告)号：US11281989B2
公开(公告)日：2022-03-22
申请号：US15451428
申请日：2017-03-07
Applicant: SAP SE
Inventor： Daniel Hermann Richard Dahlmeier , Ruidan He , Wenya Wang , Kham Sian Mung , Mohamed Yusuf Abdul Gafoor , Yi Qing Isaac New , Weile Chen , Hang Guo , Haodan Yang , Abraham Sasmito Adibowo
Abstract: Described herein is a machine learning framework for facilitating engagements. In accordance with one aspect of the framework, a machine learning model is trained based on the training data. A recommendation associated with an opportunity record may then be generated using the trained machine learning model. Results of one or more actions performed in response to the recommendation may be collected and fed back to the machine learning model to be used as the training data.
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公开(公告)号：US20200167419A1
公开(公告)日：2020-05-28
申请号：US16200829
申请日：2018-11-27
Applicant: SAP SE
Inventor： Ruidan He
Abstract: Methods, systems, and computer-readable storage media for receiving a set of document-level training data including a plurality of documents, each document having a sentiment label associated therewith, receiving a set of aspect-level training data including a plurality of aspects, each aspect having a sentiment label associated therewith, training the aspect-level sentiment classifier including a long short-term memory (LSTM) network, and an output layer using one or more of pretraining, and multi-task learning based on the document-level training data and the aspect-level training data, pretraining including initializing parameters based on pretrained weights that are fine-tuned during training, and multi-task learning including simultaneous training of document-level classification and aspect-level classification, and providing the aspect-level sentiment classifier for classifying one or more aspects in one or more sentences of one or more input documents based on sentiment classes.
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公开(公告)号：US20180285344A1
公开(公告)日：2018-10-04
申请号：US15478363
申请日：2017-04-04
Applicant: SAP SE
Inventor： Ruidan He , Daniel Dahlmeier
IPC: G06F17/27
CPC classification number: G06F17/2785
Abstract: Methods, systems, and computer-readable storage media for receiving a vocabulary that includes text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file, providing word embeddings based on the vocabulary, the word embeddings including word vectors for words included in the vocabulary, clustering word embeddings to provide a plurality of clusters, each cluster representing an aspect inferred from the vocabulary, determining a respective association score between each word in the vocabulary and a respective aspect, and providing a word ranking for each aspect based on the respective association scores.
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