Preference Learning in Terminology Extraction: A ROC-based approach

Details Preference Learning in Terminology Extraction: A ROC-based approach Preference Learning in Terminology Extraction: A ROC-based approach

2005-12-13

arxiv.org/abs/cs/0512050v1

Proceeedings of Applied Stochastic Models and Data Analysis (2005) 209-219

Computer Science

Learning

Scientific paper

A key data preparation step in Text Mining, Term Extraction selects the terms, or collocation of words, attached to specific concepts. In this paper, the task of extracting relevant collocations is achieved through a supervised learning algorithm, exploiting a few collocations manually labelled as relevant/irrelevant. The candidate terms are described along 13 standard statistical criteria measures. From these examples, an evolutionary learning algorithm termed Roger, based on the optimization of the Area under the ROC curve criterion, extracts an order on the candidate terms. The robustness of the approach is demonstrated on two real-world domain applications, considering different domains (biology and human resources) and different languages (English and French).

Affiliated with

Also associated with

No associations

Rating

Preference Learning in Terminology Extraction: A ROC-based approach 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 Preference Learning in Terminology Extraction: A ROC-based approach, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Preference Learning in Terminology Extraction: A ROC-based approach will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-36214