Graph-based methods are one of the widely used unsupervised approaches for keyword extraction. In this approach, words are linked according to their co- occurrences within the document. Afterwards, graph-based ranking...Graph-based methods are one of the widely used unsupervised approaches for keyword extraction. In this approach, words are linked according to their co- occurrences within the document. Afterwards, graph-based ranking algorithms are used to rank words and those with the highest scores are selected as keywords. Although graph-based methods are effective for keyword extraction, they rank words merely based on word graph topology. In fact, we have various prior knowledge to identify how likely the words are keywords. The knowledge of words may be frequency-based, position-based, or semantic- based. In this paper, we propose to incorporate prior knowledge with graph-based methods for keyword extraction and investigate the contributions of the prior knowledge. Experiments reveal that prior knowledge can significantly improve the performance of graph-based keyword extraction. Moreover, by combining prior knowl- edge with neighborhood knowledge, in experiments we achieve the best results compared to previous graph-based methods.展开更多
文摘Graph-based methods are one of the widely used unsupervised approaches for keyword extraction. In this approach, words are linked according to their co- occurrences within the document. Afterwards, graph-based ranking algorithms are used to rank words and those with the highest scores are selected as keywords. Although graph-based methods are effective for keyword extraction, they rank words merely based on word graph topology. In fact, we have various prior knowledge to identify how likely the words are keywords. The knowledge of words may be frequency-based, position-based, or semantic- based. In this paper, we propose to incorporate prior knowledge with graph-based methods for keyword extraction and investigate the contributions of the prior knowledge. Experiments reveal that prior knowledge can significantly improve the performance of graph-based keyword extraction. Moreover, by combining prior knowl- edge with neighborhood knowledge, in experiments we achieve the best results compared to previous graph-based methods.
