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.
Spotting keywords in handwritten documents without transcription is a valuable method as it allows one to search, index, and classify such documents. In this paper we show that keyword spotting based on bi-directional Long Short-Term Memory (BLSTM) recurrent neural nets can successfully be applied on online
This paper presents an overview of the emerging field of emotion detection from text and describes the current generation of detection methods that are usually divided into the following three main categories: keyword-based, learning-based, and hybrid recommendation approaches. Limitations of current detection methods
results in up to 1.1% absolute Word Error Rate (WER) improvement as compared to keyword-based approaches. The proposed approach reduces the WER by 6.3% absolute in our experiments, compared to an in-domain LM without considering any Web data.
mechanisms with a traditional indexing method. The goal is to identify a higher semantic content and more meaningful keyword combinations, considering both supervised and unsupervised techniques. Within a specific implementation both Bayesian learning as well as clustering are integrated to support a boost parameter towards
query-keywords are used as a basis for sentence extraction. Results obtained from experiments performed have shown that such a combined approach can provide very interesting similarity calculation and re-ranking measure. This can be used with reasonable efficiency to detect duplications on search results generated by
keywords from Web documents and to associate locations with them. This method is called location tagging. In this paper we present a location tagging approach for unstructured documents which utilizes multiple external location providers. Detected locations are ranked according to their relevance for the document, in order to
largely rely on keywords instead of geometry figure images. This study focuses on plane geometry figure (PGF) image retrieval with the aim of retrieving relevant geometry images that contain more structural information than a question text stem. To fully use geometrical properties, a Bag-of-shapes (BoS) method is proposed to
A large number of semistructured documents exist on the Web. We can find pages that contain keywords by using a search engine. But when we want to obtain information about an object like a notebook computer with 1 GB memory, a method is needed that automatically extracts attribute name (in this example
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.