A hidden Markov model‐based approach for extracting information from web news

This paper aims to present a method based on hidden Markov models (HMM) for extracting information from web news.

The samples under study are derived from the contents of PROC “People's Daily Online,” a web‐based news publication containing non‐structured archives. This study focuses on developing HMM‐based tools for news filtering in order to retrieve terms of interest, such as “Geo‐location,” “System,” and “Personas.” The experiments are performed in two stages. In the first stage, each HMM being built is exclusively serving for extracting unique target term in order to evaluate the fundamental information extraction (IE) capability. In the second stage, the experiment is then extended to resolve a more complex, multi‐term extraction issue.

The results reveal that, by using HMMs as a basis, the accuracies (F‐measure) for unique IE tasks can achieve more than 70 per cent on average, while no fewer than 66 per cent accuracies are obtained for multi‐term extraction.

The study reveals the promising of using HMM for developing automatic tool in filtering free‐structured data.