9195365
Accessibility of information on the Web
The publicly indexable WorldWide Web now contains about 800 million pages, encompassing about 6 terabytes of text data on about 3 million servers. The web is increasingly being used in all aspects of society ; for example, consumers use search engines to locate and buy goods, or to research many decisions (such as choosing a holiday destination, medical treatment or election vote). Scientists are increasingly using search engines to locate research of interest: some rarely use libraries, locating research articles primarily online; scientific editors use search engines to locate potential reviewers. Web users spend a lot of their time using search engines to locate material on the vast and unorganized web. About 85% of users use search engines to locate information 1 , and several search engines consistently rank among the top ten sites accessed on the web 2. The Internet and the web are transforming society, and the search engines are an important part of this process. Delayed indexing of scientific research might lead to the duplication of work, and the presence and ranking of online stores in search-engine listings can substantially affect economic viability (some websites are reportedly for sale primarily based on the fact that they are indexed by Yahoo). We previously estimated 3 that the publicly indexable web contained at least 320 million pages in December 1997 (the publicly indexable web excludes pages that are not normally considered for indexing by web search engines, such as pages with authorization requirements, pages excluded from indexing using the robots exclusion standard , and pages hidden behind search forms). We also reported that six major public search engines (AltaVista, Excite, HotBot, Infoseek, Lycos and Northern Light) collectively covered about 60% of the web. The largest coverage of a single engine was about one-third of the estimated total size of the web. We have now obtained and analysed a random sample of servers to investigate the amount and distribution of information on the web. During 2–28 February 1999, we chose random Internet Protocol (IP) addresses, and tested for a web server at the standard port. There are currently 256 4 (about 4.3 billion) possible IP addresses (IPv6, the next version of the IP protocol which is under development, will increase this substantially); some of these are unavailable while some are known to be unassigned. We have tested random IP addresses (with replacement), and have estimated the total number of web …
5994026
Extraction and search of chemical formulae in text documents on the web
Often scientists seek to search for articles on the Web related to a particular chemical. When a scientist searches for a chemical formula using a search engine today, she gets articles where the exact keyword string expressing the chemical formula is found. Searching for the exact occurrence of keywords during searching results in two problems for this domain: a) if the author searches for CH4 and the article has H4C, the article is not returned, and b) ambiguous searches like "He" return all documents where Helium is mentioned as well as documents where the pronoun "he" occurs. To remedy these deficiencies, we propose a chemical formula search engine. To build a chemical formula search engine, we must solve the following problems: 1) extract chemical formulae from text documents, 2) index chemical formulae, and 3) designranking functions for the chemical formulae. Furthermore, query models are introduced for formula search, and for each a scoring scheme based on features of partial formulae is proposed tomeasure the relevance of chemical formulae and queries. We evaluate algorithms for identifying chemical formulae in documents using classification methods based on Support Vector Machines(SVM), and a probabilistic model based on conditional random fields (CRF). Different methods for SVM and CRF to tune the trade-off between recall and precision forim balanced data are proposed to improve the overall performance. A feature selection method based on frequency and discrimination isused to remove uninformative and redundant features. Experiments show that our approaches to chemical formula extraction work well, especially after trade-off tuning. The results also demonstrate that feature selection can reduce the index size without changing ranked query results much.
6455989
Winners don't take all: Characterizing the competition for links on the web
As a whole, the World Wide Web displays a striking “rich get richer” behavior, with a relatively small number of sites receiving a disproportionately large share of hyperlink references and traffic. However, hidden in this skewed global distribution, we discover a qualitatively different and considerably less biased link distribution among subcategories of pages—for example, among all university homepages or all newspaper homepages. Although the connectivity distribution over the entire web is close to a pure power law, we find that the distribution within specific categories is typically unimodal on a log scale, with the location of the mode, and thus the extent of the rich get richer phenomenon, varying across different categories. Similar distributions occur in many other naturally occurring networks, including research paper citations, movie actor collaborations, and United States power grid connections. A simple generative model, incorporating a mixture of preferential and uniform attachment, quantifies the degree to which the rich nodes grow richer, and how new (and poorly connected) nodes can compete. The model accurately accounts for the true connectivity distributions of category-specific web pages, the web as a whole, and other social networks.
14241577
How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies
Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NARX networks perform much better than conventional recurrent neural networks for learning certain simple long-term dependency problems. The intuitive explanation for this behavior is that the output memories of a NARX network can be manifested as jump-ahead connections in the time-unfolded network. These jump-ahead connections can propagate gradient information more efficiently, thus reducing the sensitivity of the network to long-term dependencies. This work gives empirical justification to our hypothesis that similar improvements in learning long-term dependencies can be achieved with other classes of recurrent neural network axchitectures simply by increasing the order of the embedded memory. In particular we explore the impact of learning simple long-term dependency problems on three classes of recurrent neural network architectures: globally recurrent networks, locally recurrent networks, and NARX (output feedback) networks.Comparing the performance of these architectures with different orders of embedded memory on two simple long-term dependencies problems shows that all of these classes of network architectures demonstrate significant improvement on learning long-term dependencies when the orders of embedded memory are increased. These results can be important to a user comfortable with a specific recurrent neural network architecture because simply increasing the embedding memory order of that architecture will make it more robust to the problem of long-term dependency learning.
2529208
An experimental comparison of recurrent neural networks
Many different discrete-time recurrent neural network architectures have been proposed. However, there has been virtually no effort to compare these architectures experimentally. In this paper we review and categorize many of these architectures and compare how they perform on various classes of simple problems including grammatical inference and nonlinear system identification.