Investigating Keyphrase Indexing with Text Denoising

Details Investigating Keyphrase Indexing with Text Denoising Investigating Keyphrase Indexing with Text Denoising

2012-04-10

arxiv.org/abs/1204.2231v1

Computer Science

Digital Libraries

The full paper submitted to 12th ACM/ IEEE-CS Joint Conference on Digital Libraries (JCDL2012)

Scientific paper

In this paper, we report on indexing performance by a state-of-the-art keyphrase indexer, Maui, when paired with a text extraction procedure called text denoising. Text denoising is a method that extracts the denoised text, comprising the content-rich sentences, from full texts. The performance of the keyphrase indexer is demonstrated on three standard corpora collected from three domains, namely food and agriculture, high energy physics, and biomedical science. Maui is trained using the full texts and denoised texts. The indexer, using its trained models, then extracts keyphrases from test sets comprising full texts, and their denoised and noise parts (i.e., the part of texts that remains after denoising). Experimental findings show that against a gold standard, the denoised-text-trained indexer indexing full texts, performs either better than or as good as its benchmark performance produced by a full-text-trained indexer indexing full texts.

Affiliated with

Also associated with

No associations

Rating

Investigating Keyphrase Indexing with Text Denoising does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Investigating Keyphrase Indexing with Text Denoising, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Investigating Keyphrase Indexing with Text Denoising will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-645811