Name
Abstract | ||
|---|---|---|
We describe our method for extracting keyphrases from scientific articles which we participate in the shared task of SemEval-2 Evaluation Exercise. Even though general-purpose term extractors along with linguistically-motivated analysis allow us to extract elaborated morphosyntactic variation forms of terms, a naïve statistic approach proposed in this paper is very simple and quite efficient for extracting keyphrases especially from well-structured scientific articles. Based on the characteristics of keyphrases with section information, we obtain 18.34% for f-measure using top 15 candidates. We also show further improvement without any complications and we discuss this at the end of the paper. |
Year | Venue | Keywords |
|---|---|---|
2010 | SemEval@ACL | general-purpose term extractor,scientific article,shared task,semeval-2 evaluation exercise,statistic approach,morphosyntactic variation form,section information,well-structured scientific article,linguistically-motivated analysis |
Field | DocType | Citations |
Information retrieval,Statistic,Computer science,Artificial intelligence,Natural language processing | Conference | 1 |
PageRank | References | Authors |
0.36 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jungyeul Park | 1 | 19 | 8.13 |
| Jong Gun Lee | 2 | 85 | 5.28 |
| Béatrice Daille | 3 | 306 | 34.40 |