While a person designs a choice question with multiple options, he or she requires some incorrect options as well as correct option. And incorrect options need to be looked like correct so that the correct option cannot be easily selected out. Question answering(QA) can hardly provide incorrect options because it only focuses on searching correct answer. This paper gives a method to generate incorrect options for a certain question and a given correct option. Confusion degree is proposed to describe the degree of an incorrect option being mistakenly considered as correct. Experiment shows that the method can effectively generate incorrect and confusing options for a question. The method can help design choice questions in an examination.
This paper verifies a probable link between collaboration link and citation link through experiments on scientific papers. The result shows that scholars tend to cooperate with scholars with lower citation numbers. More than 70% of the cooperation between high-citation scholars and low-citation scholars comes from the same institution.
This paper surveys research on the Probabilistic Resource Space Model (P-RSM), which is a probabilistic, multidimensional classification space model. It can effectively manage various uncertain resources with a complete theoretical basis. This paper introduces the basic definition, paradigms, operations, constraints of this model, and discusses its usefulness through application instances.
Recommending appropriate collaborators to researchers can promote their research. In many cases, however, it is difficult for researchers to find proper collaborators from large number candidates. This paper proposes a scientific collaborator recommendation approach based on the semantic link networks, where nodes are authors, papers and interests indicated by keywords, and semantic links are write links, cite links, and contain links between these semantic nodes. Five semantic paths on the semantic link networks are proposed for deriving future collaboration between authors. Experiments on three datasets of scientific journal papers show that our method achieves good performance in predicting future collaborators. Comparing the combinations of five semantic paths reaches the following results: (1) co-author relationship, keyword information, and citation relationship play an important role in finding appropriate collaborators; and, (2) combining all the five semantic paths can get the best results on collaborator recommendation task.
With the rapid development of natural language and the implementation of the Wisdom Court, intelligent judicial assistants has become a new application of natural language processing in the judicial field. The text classification method based on word vector and deep neural network implements statistical classification of judicial documents, but it can not achieve the inherent logical interpretation of judicial cases. A method of extracting semantic logic tree from judicial case texts is proposed, and event tree can be interpreted by deep forest. Judicial documents are divided into several sub tree fragments by sentence segmentation, and each sub-tree fragment is analyzed by dependency syntax to obtain core subject-predicate-object triples. TF-IDF algorithm is used to calculate the weights of triples, and get the core sub-event sequence, and use pruning algorithm to construct the max heap. The designed triple encoding algorithm realizes max heap vectorization of event tree, and embedded deep forest algorithm to realize classification discrimination of judicial text event tree. The experimental results show that the proposed event tree construction method combined with the deep forest algorithm can greatly improve the logical interpretation and accuracy of judicial text.
