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Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, but properties of features have not been well investigated. In most cases, a group of
Purely keyword-based text search is not satisfactory because named entities and WordNet words are also important elements to define the content of a document or a query in which they occur. Named entities have ontological features, namely, their aliases, classes, and identifiers. Words in WordNet also have ontological
This work presents a type of method to process automatic summarization. And the method is a kind of trainable summarizer, in which the several characteristics considered such as sentence position, positive keyword, the center of negative keyword, title with similar sentence, sentence included in name entity, sentence
The content of a text is mainly defined by keywords and named entities occurring in it. In particular for news articles, named entities are usually important to define their semantics. However, named entities have ontological features, namely, their aliases, types, and identifiers, which are hidden from their textual
The following paper proposes a structured, natural language specification of web service behavior based on keywords in context. The advantage of this type of document is that it can be automatically analyzed in order to extract test cases for testing the web service. The test cases define not only what to test, i.e.
learning approach. We use a graphical model, Dynamic Conditional Random Fields (DCRFs), for training our classifier. Our approach is based on semantic analysis of text to classify the predicates describing coexpression relationship rather than detecting the presence of keywords. We compared our results of sentence
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