The goal of entity relation extraction is to extract entities and the relations between entities from unstructured texts. Most of the existing research approaches are oriented towards common entity labels in general-purpose domains (e.g., time, place, person, institution, etc.) and simple texts in specialized domains (texts consisting of single sentences with low knowledge density). The existence of long-distance dependencies of entity pairs (cross-sentence entity pairs) and the phenomenon of overlapping relations (different relations sharing the same entity) in complex texts are ignored. However, complex texts are common in practical applications, especially in professional fields such as power technology standards, where the knowledge density of texts is high and the phenomenon of entity-pair cross-sentence dependency is significant. To solve the above problems, this paper proposes a novel multi-hop automatic question-and-answer-based entity relation extraction method, which combines the current well-established machine reading comprehension framework with the automatic question construction mechanism proposed in this paper, and uses the a priori knowledge provided by the question as the extraction type guide, and uses the multi-hop question-and-answer mechanism to reason about the answer span of the question, effectively alleviating the phenomena of overlapping relations and entity dependence on crosssentences in complex texts. We conducted extensive comparative experiments on the power technology standard dataset self-constructed in this paper, and the results show that the MT-auQA model proposed in this paper achieves optimal performance
Intelligent problem-solving technology is a typical application of artificial intelligence in the educational field. The purpose of intelligent problem-solving is to enable machine to solve problems like human beings and help people to find useful and accurate information in the test-questions. Correct understanding of test-questions is one of the key techniques of intelligent problem-solving. The existing methods are mainly to extract simple relations from text and graphs of test-questions. In the absence of text, deep understanding of graph elements and complex relationships are difficult, which is a challenge for machines. However, the information contained in motion test-question graphs is more abundant than that in the text, which is more beneficial for machine to solve problems. To solve this problem, based on the graph classification and characteristics analysis on test-questions, a novel automatic and weak text-dependent information extraction and thorough understanding method for test-question graph of junior high school physical mechanical motion is proposed (TQGIEU). It can extract information and understand regarding to test-question graphs based on several image preprocessing techniques and neural network topologies combined with the characteristics of test-question graphs, without relying on text processing. It will generate a readable mechanical motion solution at last. It fills the blank of automatic information extraction and thorough understanding for the test-question research of physical mechanical motion. The experimental results show that the accuracy of graph classification and line segmentation model are 99.60% and 90.87% respectively. The average accuracy of TQGIEU on test dataset is 83.08%, and the total F1 score of TQGIEU is 0.859, indicating that TQGIEU can provide a good information extraction and thorough understanding service for test-question graphs of junior high school physical mechanical motion with high performance.
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