| dc.description.abstract | With the rapid development of Internet information technology and the popularity of mobile devices, access to information from web pages has become a trend, but how to extract useful information from rich and diverse information becomes a major challenge. The development of information extraction technology has gradually become a popular research topic, its main purpose is through the sorting、screening, unstructured information will be integrated into a structured data, and finally can effectively extract useful information. In this study, we hope to develop a system to automatically extract restaurant type from the FOOD board of PTT of the largest BBS web site in Taiwan through the Machine Learning Method in information extraction technology, so that users can get more convenient and fast access restaurant information
This paper is divided into three parts, the first part is pre-processing, we extract the articles from the PTT FOOD site by the PTT Crawler and then format the data; based on the extracted articles, we analysis of the keyword by statistical from the article to extract the Title、Restaurant Name、Telephone、Address and URL information; The second part is restaurant type extraction; by pre-processing analysis, we know that 72.5% of the restaurant type was implied in the title; we segmented the extracted title data through the CKIP System, and then refer to the results for manual labeling. We used WIDM_NER_TOOL which bundled CRF++ package to train the labeled data and BISEO markers to train an extraction model, the input data are used to capture the restaurant type after the model′s testing process. The last part of the article is experiment, we used the labeled data for supervised learning and used unlabeled data for Semi-Supervised to evaluate system performance. Finally we got a good result from experiment result that used this method in restaurant type extraction. | en_US |