Information Extraction (IE)

Abstract

In this paper, we propose a hybrid machine learning approach to Information Extraction by combining conventional text classification techniques and Hidden Markov Models (HMM). A text classifier generates a (locally optimal) initial output, which is refined by an HMM, providing a globally optimal classification. The proposed approach was evaluated in two case studies and the experiments revealed a consistent gain in performance through the use of the HMM. In the first case study, the implemented prototype was used to extract information from bibliographic references, reaching a precision rate of 87.48% in a test set with 3000 references. In the second case study, the prototype extracted information from author affiliations, reaching a precision rate of 90.27% in a test set with 300 affiliations.

Recommendation Systems have become an important toolto cope with the information overload problem by acquiring information about the user behavior. However, the process of getting user personal data may vary in many different ways, and can be done implicitly (through actions) or explicitly (through rates). After tracing actions or getting rates of the user, Computational Recommendation Technologies use information filtering techniques to recommend items. In this paper we describe an approach to improve the recommendation quality in the first moments the user interacts with the system. The main idea is: (1) first of all, we describe the items with the general users opinion about them; and (2) after this, we use modal symbolic structures to save this content in the user profile. The proposed methodology outperforms, concerning the Find Good Items task measured by halflife utility metric, other approaches based on the following techniques: Cognitive Filtering, Social Filtering and hybrid methods.