Baidu Online Network Technology (beijing) Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Other

China Building Materials Academy Co., Ltd.

Mingming Sun

Ping Li

Miao Fan

Haifeng Wang

Chao Feng

Yue Feng

Xu Liang Li

Minlong Peng

Zehao Dou

Sen Ye

Jing Wang

Xin Wang

Min Wang

Yang Yu

Wen Ji Huang

Jingyuan Zhang

Gang Qiao

Yun Liu

Lin Guo

Guang Yao

Xin Shen

Jingyi Yang

Zhaijun Wen

Kefeng Meng

Kunyue Zhang

Jinxing Yu

Ao Liu

Wutao Lin

Yunfeng Cai

Tan Yu

Tingting Bao

Xianshu Gao

Zhongcheng Ma

Guanbao Tang

Xianbin Wang

Qiaofei Wang

Suihua Guo

An image segmentation method includes: obtaining an image to be segmented containing a target object; performing at least one image feature fusion processing on associated feature points based on the image to be segmented, and extracting global feature information during each image feature fusion processing, wherein the associated feature points are at least two feature points having a location association relation; and determining, based on the global feature information extracted, a segmentation mask for the target object.

Image segmentation method, device and medium

There is provided a sentiment parsing method and apparatus, an electronic device, and a storage medium, which relates to the technical field of artificial intelligence such as machine learning and natural language processing. A specific implementation solution involves: identifying a role of a sentiment parsing object in a specified statement; trimming the specified statement based on the role of the sentiment parsing object to acquire pruning result information after the trimming; and parsing sentiment of the sentiment parsing object in the specified statement based on the pruning result information.

Sentiment parsing method, electronic device, and storage medium

A method for preparing low-background cement includes: uniformly mixing a seed crystal of cement, CAF whiskers, and high-magnesium raw material to yield a first mixture, calcining the first mixture at 1400-1500° C., to yield a low-background clinker, the first mixture including 1.0-5.0 wt. % of the seed crystal of cement, 1.0-5.0 wt. % of the CAF whiskers, and the balance is the high-magnesium raw material; and grinding a second mixture of the low-background clinker and gypsum, to yield low-background cement. The seed crystal of cement is a high-magnesium and low hydration heat clinker, has a specific activity of Ra-226 radioactive nuclides within 50 Bq/kg, and the MgO content of the clinker is between 4.0 wt. % and 5.0 wt. %, with 50 wt. %≤CS≤55.0 wt. %; and the high-magnesium raw material has a MgO content between 2.5 wt. % and 3.0 wt. %.

Method for preparing low-background cement

An information search method is provided. The method includes: searching for webpages related to a search request through a search engine; extracting respective texts related to the search request from respective webpages and splicing the texts to obtain a spliced text; obtaining a text segment from the spliced text; and sending the obtained text segment to the search engine, to display the obtained text segment in an information search result through the search engine. The present disclosure can bring great advantages to the search engine in terms of user experience and interaction, and can satisfy user requirements for a function of an intelligent question and answer. Through present disclosure, it is beneficial to directly presenting a short text with higher relevance to the search request to the user, thereby saving time in screening information for the user.

Information search method, device, apparatus and computer-readable medium

A method for discovering causality from data includes acquiring to-be-processed data, and obtaining a covariance matrix of the to-be-processed data; determining a first target column in the covariance matrix, taking the number of columns of the first target column as a first place in a rearrangement sequence, and obtaining a first upper triangular matrix according to the first target column; determining a position of the number of columns of the covariance matrix other than the first target column except the first place in the rearrangement sequence according to the first target column and the first upper triangular matrix, and obtaining an upper triangular matrix in each position determination; obtaining an adjacency matrix according to an upper triangular matrix and a rearrangement sequence obtained in final position determination; and generating directed acyclic graph (DAG) by using the adjacency matrix, and taking the DAG as causality discovery result of the to-be-processed data.

Method for discovering causality from data, electronic device and storage medium

The present disclosure provides a method for selecting an annotated sample. The method includes: determining a first attribute and a second attribute of a sample characteristic; in which the first attribute is a characteristic attribute of the sample characteristic in a source field sample set, and the second attribute is a characteristic attribute of the sample characteristic in a target field sample set; and determining a target annotated sample from a plurality of candidate annotated samples of the source field sample set according to the first attribute and the second attribute; in which the target annotated sample is configured to train a classification model, the classification model includes a model for determining an emotion polarity by analyzing an input sample to be classified.

Method for selecting annotated sample, apparatus, electronic device and storage medium

Embodiments of the present disclosure provide a method and a device for evaluating a comment quality, an electronic device, and a computer readable storage medium. The method includes: selecting a metadata key associated with a comment of an object from metadata of the object, the metadata including a plurality of key-value pairs; determining a value corresponding to the metadata key based on the metadata; and evaluating the comment quality based on the comment and the value corresponding to the metadata key.

Method and device for evaluating comment quality, and computer readable storage medium

Embodiments of the present disclosure provide a method and apparatus for presenting information. The method may include: acquiring target release information and a comment information set associated with the target release information; and generating, for comment information in the comment information set, usefulness probabilities and predicted comment scores of the comment information based on the comment information and the target release information. The method may further include: presenting, based on obtained usefulness probability set and predicted comment score set, the comment information in the comment information set.

Method and apparatus for presenting information

Embodiments of the present disclosure provide a method for generating an information assessment model, a method for determining the usefulness of comment information, apparatus, electronic device, and computer-readable medium. The method may include: acquiring training samples, the training samples including first sample comment information with a usefulness label and second sample comment information without a usefulness label; acquiring a predictor model and a discriminator model respectively constructed based on a generative network and a discrimination network in a generative adversarial network, and pre-training the predictor model using the first sample comment information, the predictor model being used to predict a usefulness label of a piece of comment information, the discriminator model being used to discriminate authenticity of a usefulness label; and training the predictor model and the discriminator model by iteratively performing a plurality of times of training operations, using the trained predictor model as an information assessment model.

Method and apparatus for generating information assessment model

A method, an apparatus, a computer device and a readable medium for knowledge hierarchical extraction of a text are disclosed. The method comprises: performing word segmentation on a designated text to obtain a word list, the word list including at least one word arranged in a sequence in the designated text; analyzing part-of-speech of each word in the word list in the designated text, to obtain a part-of-speech list corresponding to the word list; predicting a SPO triple included in the designated text according to the word list, the part-of-speech list and a pre-trained knowledge hierarchical extraction model. By the technical solutions, the SPO triple included in any designated text however loose its organization and structure is may be accurately extracted based on the pre-trained knowledge hierarchical extraction model. Compared to the prior art, the efficiency and accuracy of knowledge hierarchical extraction may be effectively improved.

Method, apparatus, computer device and readable medium for knowledge hierarchical extraction of a text

A method and an apparatus for generating a model are provided. The method includes: acquiring a sample set including sample sentences and labeling knowledge corresponding thereto; and selecting a sample from the sample set, and performing following training steps: inputting a sample sentence into a first initial model to generate first prediction knowledge corresponding to the sample sentence; inputting the first prediction knowledge into a second initial model to generate a first prediction sentence corresponding to the first prediction knowledge; inputting labeling knowledge into the second initial model to generate a second prediction sentence corresponding to the labeling knowledge; inputting the second prediction sentence into the first initial model to generate a second prediction knowledge corresponding to the second prediction sentence; determining a first reward signal; and training, using a reinforcement learning method based on the first reward signal to obtain a first model.

Method and apparatus for generating model

According to embodiments of the present disclosure, a method and an apparatus for responding to a question, and a storage medium are provided. The method includes: determining a question characteristic representation corresponding to a question for an object; determining a comment characteristic representation corresponding to a first comment for the object; generating a first target characteristic representation by utilizing the question characteristic representation and the comment characteristic representation; and determining an answer for the question based on the first target characteristic representation.

Method and apparatus for responding to question, and storage medium

Embodiments of the present disclosure provide a method, an apparatus, a device and a storage medium for evaluating quality of an answer. The method includes extracting a question feature expression of a question and an answer feature expression of an answer with respect to the question, the question and the answer being represented in a form of text; determining a measurement of textual quality of the answer based on the answer feature expression; determining a measurement of correlation on semantics between the question and the answer based on the question feature expression and the answer feature expression; and determining a quality score of the answer with respect the question based on the measurement of textual quality and the measurement of correlations. Therefore, a high-quality answer may be accurately obtained.

Method, apparatus, device and storage medium for evaluating quality of answer

According to an exemplary embodiment of the present disclosure, a method, apparatus for evaluating a review, a device and a computer readable storage medium are provided. The method for evaluating a review includes obtaining a first vectorized representation of a set of text items in a review for a target object. The method further includes extracting a semantic feature of at least two consecutive text items of the set of text items based on the first vectorized representation. The method further includes determining a degree of importance of the at least two consecutive text items in a context of the review, and determining a degree of the review helping a user to evaluate the target object, based on the degree of importance and the semantic feature. In this way, an automatic, efficient, and accurate evaluation of the value of a review may be achieved.

Method, apparatus for evaluating review, device and storage medium

A method, apparatus and device are for extracting information. The method includes: acquiring an annotated corpus, which includes a plurality of sample sentences and annotated information sets corresponding to the sample sentences, constructing an input sequence and an output sequence based on the sample sentences and the annotated information sets corresponding to the sample sentences, obtaining an information extraction model generating the output sequence from the input sequence by carrying out training with a deep learning method, and inputting a to-be-processed sentence into the information extraction model to extract a knowledge information set included in the to-be-processed sentence. The annotated information set includes information of at least one piece of the following types of knowledge to be extracted from corresponding sample sentences: knowledge based on verbs or prepositions, knowledge based on noun attributes, knowledge of entity description, and knowledge of a relationship between an entity and a concept.

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Beijing, China

Mingming Sun