National University of Defense Technology

Other

Taihua Shao

Fei Cai

Honghui Chen

Wanyu Chen

Aimin Luo

Siyuan Wang

Mengru Wang

Dengfeng Liu

Zhen Shu

Tao Chen

Jianming Zheng

Xin Zhang

Chengyu Song

Peihong Li

Junxian Liu

Miao Jiang

Yanying Mao

Jingjing Yan

Mengmeng Zhang

Danyang Jiang

Chonghao Chen

Yupu Guo

A method and an apparatus for few-shot relation classification and filtering, and a device are provided. The method includes: constructing a coarse-grained filter for filtering an unlabeled corpus to obtain candidate instances with a same entity as a seed instance and with similar semantics to the seed instance; constructing a fine-grained filter for filtering the candidate instances to obtain a candidate instance with a same relation concept as the seed instance; defining the candidate instance as a positive instance set, and defining candidate instances with different relation concepts from the seed instance as a negative sample set; constructing a false positive instance correction module for adjusting and controlling a proportion of the negative sample set used by a classifier during training; training the classifier based on a small number of obtained labeled instances belonging to a newly emerging relation and the adjusted positive instance set and negative sample set.

Method and apparatus for few-shot relation classification and filtering, and device

A method for predicting matching degree between a resume and a post, and a related device are provided in this disclosure. In the method for predicting the matching degree between the resume and the post, and the related device according to this disclosure, firstly the semi-structured keys and values in post information and resume information and their source are obtained. Then, the matching degree between the resume information and the post information is predicted by a prediction model including a cascaded pre-trained language model, a Transformer encoder and a single label classification model, based on the keys and values of a respective post information and resume information attribute, and corresponding source representations. Thus, by comprehensively searching internal interaction and external interaction of semi-structured multivariate attributes in person-post matching, the matching result is more accurate.

Method for predicting matching degree between resume and post, and related device

A fact validation method including the following steps: a statement to be validated is inputted and a searching is made for the statement to obtain an evidence set of the statement; a hierarchical heterogeneous graph consisting of an entity node, a sentence node and a context node is constructed based on the evidence set; the statement and the evidence set are spliced and a node is initialized to obtain feature representation of the node; the feature representation of the node is updated based on inference according to a propagation direction of a neural network of the node in the hierarchical heterogeneous graph; and an inference path for the updated feature representation of the node is built and a prediction result of the statement is output according to the inference path.

Fact validation method and system, computer device and storage medium

A method, a device and electronic device for clarification question generation are provided in one or more embodiments of this disclosure. The method includes: extracting entity information from a fuzzy context input by a user; inputting the fuzzy context into a template generating module of a pre-built CQG neural network model so as to obtain a clarification question template; inputting the entity information into an entity rendering module of the CQG neural network model so as to obtain at least one entity phrase; and generating a clarification question for a fuzzy question based on the clarification question template and the at least one entity phrase for presenting to the user.

Method, system, electronic device and storage medium for clarification question generation

Disclosed are an answer selection method for a question answering system. With the method and system, a sentence distributed representation of a question and an answer is generated by adopting a hierarchical length-adaptive neural network structure with a purpose of extracting high-quality sentence features by using different neural feature extractors according to a length of an input sentence. First, one word distributed representation is generated for each word in the input sentence by connecting one fixed word embedding and one finely-adjusted word embedding. Next, a BiLSTM-based feature extractor is used for a short sentence and a Transformer-based feature extractor is used for a long sentence. Finally, a sentence vector representation for measuring a correlation between a question and a candidate answer is generated by using an attention pooling layer considering an interaction between a question sentence and an answer sentence.

Answer selection method for question answering system and the system

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Hunan, China

Taihua Shao