Ordinal Regression for Information Retrieval

Abstract

This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effectiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM significantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.

Study on Ontology-based Semantic Extraction Method for Heterogeneous Data

“…Vincini M. et al propose semantic integration method of heterogeneous data source in data transformation system [4], which to some extent achieves semantic integration of Web data. Stein R et al propose to apply SimpleDB storage technique to support semantic treatment Stratustore [5], which verifies the superiority of cloud database technology SimpleDB in processing simple semantic query response. Llora X et al raise a method to construct data-intensive cloud application environment based on semantic Web technology [6].…”