Keyphrase Annotation with Graph Co-Ranking
Table 2: Results of TopicCoRank and the baselines at 10 keyphrases for each dataset. Precision (P), Recall (R) and F-score (F) are reported in percentages. † indicates a significant F-score improvement over TopicRank and KEA++ at 0.001 level using Student’s t-test.
['[BOLD] Method', '[BOLD] Linguistics P', '[BOLD] Linguistics R', '[BOLD] Linguistics F', '[BOLD] Information Science P', '[BOLD] Information Science R', '[BOLD] Information Science F', '[BOLD] Archaeology P', '[BOLD] Archaeology R', '[BOLD] Archaeology F']
First, we observe that the assignment baseline KEA++ mostly achieves the lowest performance, which is surprising compared to the performance reported by \newcitemedelyan2006kea++. The first reason for this observation is that KEA++ is restricted to thesauri entries while most keyphrases are missing within our documents. The second reason is that KEA++ relies on rich thesauri that contain an important amount of semantic relations between the entries, while our (real application) thesauri have a modest amount of semantic relations between the entries.