Keywords Extraction based on Sentence-Ranking from Chinese Patents
Zhihong Wang, Yi Guo, Tianmei Qi · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2018
Patent, an important scientific literature, records a large amount of innovative and practical research.The patent keywords also provide a high-level topic description of a patent document and hold an important position in classic NLP tasks, such as patent classification or clustering.However, there are few research works on keywords extraction covering the Chinese patents in current stage.In this paper, we propose a novel algorithm to extract keywords from Chinese patents.A sentenceranking model, based on a sentence embedding graph and heuristic rules, is constructed to select the top-KS percent of the sentences.At the same time, the semantic-ranking weights of sentences are also transmitted to keywords extraction.The experimental results on our Chinese patents datasets testifies that the sentence-ranking based keywords extraction algorithm improves the performance by 6% to 13% in F-score.In summary, the new idea of selecting key sentences from original documents can effectively filter out noisy sentences and leverage the performance of keywords extraction.