Keyphrase Extraction with Span-based Feature Representations
Table 1: Details of candidate phrases and keyphrases. The coverage show that 9.42% to 22.84% keyphrases are not covered by candidate phrases. This will limit the final performance of our model. The ratio of ci− and ci+ is range from 9.85 to 28.85 meaning that the classification task is imbalanced.
['[BOLD] Dataset', '[BOLD] Inspec', '[BOLD] Krapivin', '[BOLD] NUS', '[BOLD] SemEval', '[BOLD] KP20k', '[BOLD] KP Training', '[BOLD] KP Validation']
We use the certain part-of-speech pattern to extract the candidate phrases inspired by \citeauthorle2016unsupervised \shortcitele2016unsupervised. (JJ|JJR|JJS|VBG|VBN)∗(NN|NNS|NNP|NNPS|VBG)+ The intersection of candidate phrases and keyphrases are positives denoted as ci+. When determining the match of two phrases, we use Porter Stemmer for preprocessing. The coverage shows that 9.42% to 22.84% keyphrases are not cover by candidate phrases. This will limit the final performance of our model. Our candidates only cover 77.16% keyphrases of NUS, the main reason is NUS contains irregular keyphrases assigned by reader. The ratio of ci− and ci+ is range from 9.85 to 28.85 meaning that data is imbalanced for classification task. Statistical indicators are close among KP dataset which shows this is a reasonable split.