With the explosive growth of network information, in order to obtain the information faster and more accurately, this paper proposes a text keyword extraction method based on Bert. Firstly, the key sentence set is extracted from the background material by Bert model as the information supplement to the text. Then, based on the extended text, TF-IDF, text rank and LDA are combined to extract keywords. The experimental results on real science and technology academic paper data sets show that the performance of the fusion multi type feature combination algorithm is better than that of the traditional single algorithm; and the F value of the algorithm is increased by 1.5% by extracting key sentences from background materials, which further improves the effect of key word extraction.
Reading comprehension Question-Answering (QA) for College Entrance Examination (Gaokao in Chinese) is a challenging AI task because it requires effective representation to capture complicated semantic relations between the question and answers. In this paper, a novel method of Chinese Automatic Question-Answering based on a graph is proposed. The method first uses the Chinese FrameNet and discourse topic (paragraph topic sentence and author’s opinion sentence) to construct the affinity matrix between the question and candidate sentences and then employs the algorithm based on the graph to iteratively calculate the importance of each sentence. At last, the top 6 candidate answer sentences are selected based on the ranking scores. The recall on Beijing College Entrance Examination in the recent twelve years is 67.86%, which verifies the effectiveness of the method.
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