基于半马尔可夫条件随机场的持续时间建模及其关键词提取
Duration modeling with semi-Markov Conditional Random Fields for keyphrase extraction
摘要：
现有的关键短语提取方法需要进行预处理以生成候选短语或进行后处理以将关键词转换为关键短语。在本文中，我们提出了一种新的方法，称为持续时间建模与半马尔可夫条件随机场（DM-SMCRF）的关键词提取。首先，基于半马尔可夫链的性质，DM-SMCRFs可以对片段级特征进行编码，并将句子中的短语顺序分类为关键短语或非关键短语。其次，通过假设状态转换和状态持续时间之间的独立性，DM-SMCRF模型的持续时间（长度）的关键短语的分布，以进一步探索状态持续时间信息，这可以帮助确定的关键短语的大小。基于由持续时间分布得到的参数化持续时间特征的凸性，提出了一种约束维特比算法来提高DM-SMCRF的译码性能。我们彻底评估了DM-SMCRF在不同领域数据集上的性能。实验结果验证了该模型的有效性。
英文摘要：
Existing methods for keyphrase extraction need preprocessing to generate candidate phrase or post-processing to transform keyword into keyphrase. In this paper, we propose a novel approach called duration modeling with semi-Markov Conditional Random Fields (DM-SMCRFs) for keyphrase extraction. First of all, based on the property of semi-Markov chain, DM-SMCRFs can encode segment-level features and sequentially classify the phrase in the sentence as keyphrase or non-keyphrase. Second, by assuming the independence between state transition and state duration, DM-SMCRFs model the distribution of duration (length) of keyphrases to further explore state duration information, which can help identify the size of keyphrase. Based on the convexity of parametric duration feature derived from duration distribution, a constrained Viterbi algorithm is derived to improve the performance of decoding in DM-SMCRFs. We thoroughly evaluate the performance of DM-SMCRFs on the datasets from various domains. The experimental results demonstrate the effectiveness of proposed model.
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