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Duration Modeling with Semi-Markov Conditional Random Fields for Keyphrase Extraction
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DOI：10.1109/TKDE.2019.2942295.png)
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Abstract
En 中文
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.
Keywords:
Feature extraction
Data mining
Hidden Markov models
Decoding
Task analysis
Training
Labeling
Keyphrase extraction
semi-Markov
duration modeling
constrained Viterbi
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