Unsupervised query segmentation using click data and dictionaries information

We describe results of experiments with an unsupervised framework for query segmentation, transforming keyword queries into structured queries. The resulting queries be used to more accurately search product databases, and potentially improve result presentation and query suggestion. The key to developing an accurate and scalable system for task is to train a query segmentation or attribute detection system over labeled data, which be acquired automatically from query and click-through logs. The main contribution of work is a improving method to automatically acquire such training data — resulting in significantly higher segmentation performance, compared to previously reported methods

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover