Table extraction for answer retrieval

The ability to find tables and extract information from them is a necessary component of many information retrieval tasks. Documents often contain tables in order to communicate densely packed, multi-dimensional information. Tables do this by employing layout patterns to efficiently indicate fields and records in two-dimensional form. Their rich combination of formatting and content presents difficulties for traditional retrieval techniques. This paper describes techniques for extracting tables from text and retrieving answers from the extracted information. We compare machine learning (especially, Conditional Random Fields) and heuristic methods for table extraction. To retrieve answers, our approach creates a cell document, which contains the cell and its metadata (headers, titles) for each table cell, and the retrieval model ranks the cells of the extracted tables using a language-modeling approach. Performance is tested using government statistical Web sites and news articles, and errors are analyzed in order to improve the system.     

A New Hot Table for Relining

Abstract

Two red dyes from 21st dynasty Egypt and one from the 12th dynasty were analyzed. The first two were essentially madder, in one case mixed with small amounts of tannins. The third contained pure haematite (red ochre), thus representing one of the earliest known uses of this pigment for the dyeing of linen.     

科技期刊目次页编排规范的探讨

为更好地规范科技期刊目次页的编排,分析了期刊出版的实践与现行有关国家标准国关于目次页编排存在的问题,并提出了相应的建议,如设置广告目次等.