Name
Abstract | ||
|---|---|---|
Keyphrases efficiently summarize a documentu0027s content and are used in various document processing and retrieval tasks. Several unsupervised techniques and classifiers exist for extracting keyphrases from text documents. Most of these methods operate at a phrase-level and rely on part-of-speech (POS) filters for candidate phrase generation. In addition, they do not directly handle keyphrases of varying lengths. We overcome these modeling shortcomings by addressing keyphrase extraction as a sequential labeling task in this paper. We explore a basic set of features commonly used in NLP tasks as well as predictions from various unsupervised methods to train our taggers. In addition to a more natural modeling for the keyphrase extraction problem, we show that tagging models yield significant performance benefits over existing state-of-the-art extraction methods. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computation and Language | Computer science,Document processing,Phrase,Natural language processing,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1608.00329 | 0 |
PageRank | References | Authors |
0.34 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sujatha Das Gollapalli | 1 | 74 | 6.24 |
| Xiao-Li Li | 2 | 74 | 9.21 |