The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Text analysis of a web page is more difficult than the analysis of the text of normal document due to the presence of additional information, such as HTML structure, styling codes, irrelevant text, and presence of hyperlinks. In this paper, we propose an unsupervised method to extract keywords from a web page. The
Language model adaptation using text data downloaded from the WWW is an efficient way to train a topic-specific LM. We are developing an unsupervised LM adaptation method using data in the Web. The one key point of unsupervised Web-based LM adaptation is how to select keywords to compose the search query. In this
This paper presents a method for generating indexable and browsable keyword metadata from ASR transcripts by leveraging theWeb. Search engine queries are built from an ASR transcript and used to retrieve similar text from the Web. The keyword meta information embedded in those pages for search engines is then ranked
decoders' outputs, an optimal hypothesis was chosen for each utterance by a topic-selection criterion minimizing an energy function with three terms: likelihood scores for the utterances; keyword co-occurrence statistics to measure the long-distance correlation; and Web-based hypothesis verification scores, which penalize
Traditional Web retrieval system is based on whole-length search of keyword, which would bring large results, but users can't find the answer that they need quickly. This paper presents a model of information retrieval based on media data, to improve the web information retrieval efficiency and precision in specific
With the advancement of internet technology, data on internet have become increasingly huge. Therefore, how to find out the valuable pages from a magnitude of information has been an urgent problem, which should be followed upright. Tradition search engines, based on the query of keywords, have no ability of
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.